{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据划分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, shutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 原始数据集解压路径\n",
    "original_dataset_dir = 'data/kaggle_original_data'\n",
    "\n",
    "# 保存较小数据集的目录\n",
    "base_dir = 'data/cats_and_dogs_small'\n",
    "os.mkdir(base_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 划分后的训练、验证、测试集目录\n",
    "train_dir = os.path.join(base_dir, 'train')\n",
    "os.mkdir(train_dir)\n",
    "validation_dir = os.path.join(base_dir, 'validation')\n",
    "os.mkdir(validation_dir)\n",
    "test_dir = os.path.join(base_dir, 'test')\n",
    "os.mkdir(test_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练集猫、狗目录\n",
    "train_cats_dir = os.path.join(train_dir, 'cats')\n",
    "os.mkdir(train_cats_dir)\n",
    "train_dogs_dir = os.path.join(train_dir, 'dogs')\n",
    "os.mkdir(train_dogs_dir)\n",
    "\n",
    "# 验证集猫、狗目录\n",
    "validation_cats_dir = os.path.join(validation_dir, 'cats')\n",
    "os.mkdir(validation_cats_dir)\n",
    "validation_dogs_dir = os.path.join(validation_dir, 'dogs')\n",
    "os.mkdir(validation_dogs_dir)\n",
    "\n",
    "# 测试集猫、狗目录\n",
    "test_cats_dir = os.path.join(test_dir, 'cats')\n",
    "os.mkdir(test_cats_dir)\n",
    "test_dogs_dir = os.path.join(test_dir, 'dogs')\n",
    "os.mkdir(test_dogs_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 拷贝 1000 张猫的图像到 train_cats_dir\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(train_cats_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "    \n",
    "# 拷贝 500 张猫的图像到 validation_cats_dir\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(validation_cats_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "\n",
    "# 拷贝 500 张猫的图像到 test_cats_dir\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(test_cats_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "\n",
    "# 拷贝 1000 张狗的图像到 train_dogs_dir\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(train_dogs_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "\n",
    "# 拷贝 500 张狗的图像到 validation_dogs_dir\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(validation_dogs_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "\n",
    "# 拷贝 500 张狗的图像到 test_dogs_dir\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(test_dogs_dir, fname)\n",
    "    shutil.copyfile(src, dst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total training cat images: 1000\n",
      "Total training dog images: 1000\n",
      "Total validation cat images: 500\n",
      "Total validation dog images: 500\n",
      "Total test cat images: 500\n",
      "Total test dog images: 500\n"
     ]
    }
   ],
   "source": [
    "print('Total training cat images:', len(os.listdir(train_cats_dir)))\n",
    "print('Total training dog images:', len(os.listdir(train_dogs_dir)))\n",
    "print('Total validation cat images:', len(os.listdir(validation_cats_dir)))\n",
    "print('Total validation dog images:', len(os.listdir(validation_dogs_dir)))\n",
    "print('Total test cat images:', len(os.listdir(test_cats_dir)))\n",
    "print('Total test dog images:', len(os.listdir(test_dogs_dir)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhikang.du\\AppData\\Local\\conda\\conda\\envs\\python36\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 148, 148, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 74, 74, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 72, 72, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 36, 36, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 34, 34, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 17, 17, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 15, 15, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               3211776   \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 3,453,121\n",
      "Trainable params: 3,453,121\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras import models\n",
    "from keras import layers\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import optimizers\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(lr=1e-4),\n",
    "              metrics=['acc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# 将所有图像乘以 1/255 缩放\n",
    "train_datagen = ImageDataGenerator(rescale=1./255)\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "# 读取训练图像生成器\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_dir, \n",
    "    target_size=(150, 150),\n",
    "    batch_size=20, \n",
    "    class_mode='binary')\n",
    "\n",
    "# 读取验证图像生成器\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "    validation_dir, \n",
    "    target_size=(150, 150), \n",
    "    batch_size=20, \n",
    "    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data batch shape: (20, 150, 150, 3)\n",
      "labels batch shape: (20,)\n"
     ]
    }
   ],
   "source": [
    "for data_batch, labels_batch in train_generator:\n",
    "    print('data batch shape:', data_batch.shape)\n",
    "    print('labels batch shape:', labels_batch.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练与验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "100/100 [==============================] - 196s 2s/step - loss: 0.6910 - acc: 0.5265 - val_loss: 0.6792 - val_acc: 0.5650\n",
      "Epoch 2/30\n",
      "100/100 [==============================] - 180s 2s/step - loss: 0.6507 - acc: 0.6190 - val_loss: 0.6258 - val_acc: 0.6510\n",
      "Epoch 3/30\n",
      "100/100 [==============================] - 158s 2s/step - loss: 0.5957 - acc: 0.6840 - val_loss: 0.6008 - val_acc: 0.6840\n",
      "Epoch 4/30\n",
      "100/100 [==============================] - 168s 2s/step - loss: 0.5547 - acc: 0.7150 - val_loss: 0.6354 - val_acc: 0.6530\n",
      "Epoch 5/30\n",
      "100/100 [==============================] - 183s 2s/step - loss: 0.5308 - acc: 0.7190 - val_loss: 0.5697 - val_acc: 0.6990\n",
      "Epoch 6/30\n",
      "100/100 [==============================] - 184s 2s/step - loss: 0.5072 - acc: 0.7480 - val_loss: 0.5784 - val_acc: 0.6800\n",
      "Epoch 7/30\n",
      "100/100 [==============================] - 179s 2s/step - loss: 0.4867 - acc: 0.7610 - val_loss: 0.5556 - val_acc: 0.7120\n",
      "Epoch 8/30\n",
      "100/100 [==============================] - 179s 2s/step - loss: 0.4507 - acc: 0.7805 - val_loss: 0.5971 - val_acc: 0.6950\n",
      "Epoch 9/30\n",
      "100/100 [==============================] - 191s 2s/step - loss: 0.4335 - acc: 0.8025 - val_loss: 0.5755 - val_acc: 0.7030\n",
      "Epoch 10/30\n",
      "100/100 [==============================] - 166s 2s/step - loss: 0.4150 - acc: 0.8090 - val_loss: 0.5481 - val_acc: 0.7220\n",
      "Epoch 11/30\n",
      "100/100 [==============================] - 158s 2s/step - loss: 0.3780 - acc: 0.8305 - val_loss: 0.5556 - val_acc: 0.7350\n",
      "Epoch 12/30\n",
      "100/100 [==============================] - 177s 2s/step - loss: 0.3578 - acc: 0.8395 - val_loss: 0.5996 - val_acc: 0.7180\n",
      "Epoch 13/30\n",
      "100/100 [==============================] - 180s 2s/step - loss: 0.3336 - acc: 0.8625 - val_loss: 0.5983 - val_acc: 0.7220\n",
      "Epoch 14/30\n",
      "100/100 [==============================] - 162s 2s/step - loss: 0.3144 - acc: 0.8660 - val_loss: 0.5558 - val_acc: 0.7440\n",
      "Epoch 15/30\n",
      "100/100 [==============================] - 176s 2s/step - loss: 0.2823 - acc: 0.8855 - val_loss: 0.6273 - val_acc: 0.7110\n",
      "Epoch 16/30\n",
      "100/100 [==============================] - 165s 2s/step - loss: 0.2685 - acc: 0.8915 - val_loss: 0.6004 - val_acc: 0.7320\n",
      "Epoch 17/30\n",
      "100/100 [==============================] - 162s 2s/step - loss: 0.2475 - acc: 0.9025 - val_loss: 0.5949 - val_acc: 0.7350\n",
      "Epoch 18/30\n",
      "100/100 [==============================] - 157s 2s/step - loss: 0.2225 - acc: 0.9140 - val_loss: 0.6441 - val_acc: 0.7270\n",
      "Epoch 19/30\n",
      "100/100 [==============================] - 152s 2s/step - loss: 0.2010 - acc: 0.9255 - val_loss: 0.7058 - val_acc: 0.7330\n",
      "Epoch 20/30\n",
      "100/100 [==============================] - 157s 2s/step - loss: 0.1753 - acc: 0.9315 - val_loss: 0.6996 - val_acc: 0.7280\n",
      "Epoch 21/30\n",
      "100/100 [==============================] - 152s 2s/step - loss: 0.1705 - acc: 0.9360 - val_loss: 0.6832 - val_acc: 0.7280\n",
      "Epoch 22/30\n",
      "100/100 [==============================] - 150s 1s/step - loss: 0.1403 - acc: 0.9505 - val_loss: 0.7621 - val_acc: 0.7320\n",
      "Epoch 23/30\n",
      "100/100 [==============================] - 151s 2s/step - loss: 0.1327 - acc: 0.9530 - val_loss: 0.7275 - val_acc: 0.7330\n",
      "Epoch 24/30\n",
      "100/100 [==============================] - 150s 1s/step - loss: 0.1088 - acc: 0.9670 - val_loss: 0.8087 - val_acc: 0.7180\n",
      "Epoch 25/30\n",
      "100/100 [==============================] - 150s 2s/step - loss: 0.0880 - acc: 0.9775 - val_loss: 0.8572 - val_acc: 0.7240\n",
      "Epoch 26/30\n",
      "100/100 [==============================] - 150s 2s/step - loss: 0.0857 - acc: 0.9755 - val_loss: 1.0720 - val_acc: 0.7100\n",
      "Epoch 27/30\n",
      "100/100 [==============================] - 152s 2s/step - loss: 0.0769 - acc: 0.9790 - val_loss: 0.9041 - val_acc: 0.7220\n",
      "Epoch 28/30\n",
      "100/100 [==============================] - 150s 2s/step - loss: 0.0670 - acc: 0.9785 - val_loss: 1.3339 - val_acc: 0.6870\n",
      "Epoch 29/30\n",
      "100/100 [==============================] - 149s 1s/step - loss: 0.0542 - acc: 0.9845 - val_loss: 1.0452 - val_acc: 0.7150\n",
      "Epoch 30/30\n",
      "100/100 [==============================] - 150s 2s/step - loss: 0.0465 - acc: 0.9855 - val_loss: 0.9860 - val_acc: 0.7260\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "    train_generator,\n",
    "    steps_per_epoch=100,\n",
    "    epochs=30, \n",
    "    validation_data=validation_generator, \n",
    "    validation_steps=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('cats_and_dogs_small_1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vv127dqSnpxcPWPbJJ58UT1bSpUsXfvnll7B/t8Ykitxcl18vOWVDq1Zw3HGJE9yD1rmLyMvA+cCPqtohwPPDgLt8i7uBG1V1eXkb9vvfQ6QnGOrcGXxxMaCGDRvStWtX3n//fQYNGsT06dO5/PLLERHS0tKYOXMmdevWZdu2bXTr1o2BAweWOo/o888/T82aNcnJySEnJ4eMjIzi58aPH89RRx1FYWEhZ5xxBjk5Odx666088cQTzJs3j0ZFxbU+S5YsYcqUKSxcuBBV5bTTTqNPnz40aNCANWvWMG3aNP76179y2WWX8dZbb5U5PvtVV13F008/TZ8+fbjvvvt44IEHmDhxIo888gjfffcd1atXL04FTZgwgWeffZYePXqwe/du0tLSQvhtG5OYcnPhf/7nyMdFXO89USpmvPTcXwH6l/H8d0AfVU0HHgQmR6BdMeOfmvFPyagqY8eOJT09nTPPPJP//ve/bN26tdTtzJ8/vzjIpqenk56eXvzcjBkzyMjIoEuXLqxYsSLooGCfffYZF110EbVq1aJ27doMHjyYT33dh1atWtG5c2eg7GGFwY0vv2PHDvr06QPA1Vdfzfz584vbOGzYMKZOnVp8JWyPHj244447mDRpEjt27LArZE3S+/VXN/FPaSNq9OoF33/v1ol3Qf9bVXW+iLQs4/kv/Ba/BJqVv1ll97Cj6cILL+SOO+7gq6++Yu/evcU97qysLPLz81myZAmpqam0bNky4DC//gL16r/77jsmTJjA4sWLadCgASNGjAi6nbLG/6nud7lcSkpK0LRMaf71r38xf/58Zs2axYMPPsiKFSsYM2YM5513HnPmzKFbt258+OGHtClZQmBMEvn2W/ezrOAOrvfeokXFtClckc65jwTeK+1JERklItkikp2fnx/hXUdG7dq16du3L9dee+1hJ1J37tzJ0UcfTWpqKvPmzWNjkI/u3r17F0+C/c0335CTkwO44YJr1apFvXr12Lp1K++9d+jXVadOnYB57d69e/POO++wZ88efv31V2bOnEmvor+yENSrV48GDRoU9/pff/11+vTpw8GDB/n+++85/fTTeeyxx9ixYwe7d+9m3bp1dOzYkbvuuovMzExW+U9NY0wSKq0MskiHDlC3bmLk3SP2PVtETscF956lraOqk/GlbTIzM2MzHKUHQ4cOZfDgwYdVzgwbNowLLriAzMxMOnfuHLQHe+ONN3LNNdeQnp5O586d6dq1K+BmVerSpQvt27c/YrjgUaNGce6559KkSRPmzZtX/HhGRgYjRowo3sZ1111Hly5dykzBlObVV19l9OjR7Nmzh9atWzNlyhQKCwsZPnw4O3fuRFW5/fbbqV+/Pn/84x+ZN28eKSkptGvXrnhWKWOSVW6uO5F60kmBn09JgR49EiO4exry15eWmR3ohKrv+XRgJnCuqn7rZcc25G9ysPfMJJNLL3UTc3xbRhR7+GEYO9ZdydqwYcW1rYjXIX/LnZYRkebA28CVXgO7McbEo9zc0lMyRYrq3T//PPrtKY+gwV1EpgELgJNFJE9ERorIaBEZ7VvlPqAh8JyILBORsi/DNMaYOFRQ4Hrswb6InnoqVKsW/6kZL9UyZV6eqarXAddFqkFF08GZ+BerWbyMiYb16+HAgeDBPS3NBfh4r3ePqytU09LS2L59uwWNBKCqbN++3S5sMkmjqBjMyymkXr0gOxvieV75uLoqpVmzZuTl5RGvZZLmcGlpaTRrFpHLGoyJuWBlkP569YJHHoFFi6Bv36g2K2xxFdxTU1Np1apVrJthjKmEcnOhSROoVy/4uv/zP244gk8/jd/gHldpGWOMiZVVq7ylZADq14eOHeP7pKoFd2NMpafqrQzSX69esGCBq7KJRxbcjTGV3pYtsGuX9547uHr33bvdRU/xyIK7MabSK232pbLE++QdFtyNMZVeKGWQRZo1g5Yt47fe3YK7MabSy82FOnVctUwoevVyPfd4vDTHgrsxptLLzXW99lAvju/VC378EdaujU67ysOCuzGm0gulDNJfPOfdLbgbYyq1nTth8+bQyiCLtGnjhv214G6MMVGyZo0rTQxVOCdTi8TzpNkW3I0xCe/XXyEjA269NfTXlie4g8u7r10LP/wQ3uujxYK7MSbhzZ3reu3TpsFPP4X22txcSE2F1q3D23fRdMbxlpqx4G6MSXhvvw01asC+ffDqq6G9NjfXzZlaNcxhFLt0gZo14y81Y8HdGJPQ9u+H2bNhyBDo3h1eeCG0uvOiMshwpaZCt27WczfGmIiaN89VvFx0EYwe7abK+/hjb6/97Tc3A1N553jv2xeWLYMpU8q3nUiy4G6MSWgzZ0KtWnDWWXDppdCggeu9e7F2LRQWhlcG6e+22+CMM+Daa+G+++LjilUL7saYhFVYCO+8AwMGuLlNa9SAESNcDn7r1uCvD2fAsEDq1oU5c1xwf/BBuOoq960gliy4G2MS1pdfuiA+ePChx264wY2x/vLLwV9fVAZ58snlb0tqKrz0Ejz0EEydCuecAz//XP7thitocBeRl0XkRxH5ppTnRUQmichaEckRkYzIN9MYY440cyZUq+Z67kVOPhn69YMXX3Q9+7Lk5kKLFi6tEwkicM89kJXlJvLo3t3l9GPBS8/9FaB/Gc+fC5zku40Cni9/s4wxpmyqLv1y5pkuLeJv9GjYuNHVv5cl1NmXvLriCvjgAzeoWLdusHBh5PcRTNDgrqrzgbIuCxgEvKbOl0B9EQlx4ExjjAlNTg58952rkilp0CA45piyT6wePBj+gGFe9O7teu916rhqmpkzo7Of0kQi594U+N5vOc/3mDHGRM3bb0OVKjBw4JHPVasGI0fCv/4FmzYFfv3338PevdEL7uBSRAsWQKdOcPHF8OSTFVdJE4ngHmgE5IDNF5FRIpItItn5+fkR2LUxprKaOdMN2nX00YGfHzXKBdKXXgr8fFGlTDTSMv6OPtrV4g8eDHfc4ca/CXYuIBIiEdzzgOP9lpsBmwOtqKqTVTVTVTMbN24cgV0bYyqjtWvh668Dp2SKtGjhTrS+9BIcOHDk85Eqg/SiRg2YMQP+8Ad45hkX5KMtEsF9FnCVr2qmG7BTVbdEYLvGGBNQUf66rOAO7sTqli0wa9aRz61a5cZir6h+ZpUq8PjjMHlyeKNXhko0SAJIRKYBfYFGwFbgT0AqgKq+ICICPIOrqNkDXKOq2cF2nJmZqdnZQVczxpgjdO/uxpRZsqTs9QoLoVUrl/v+4IPDn+vd26Vt4m1MmGBEZImqZgZbL+g4aKo6NMjzCtwUQtuMMSZsmze7i5ceeij4uikpLvf+xz+6yTxOOunQc7m5cOGF0WtnrNkVqsaYhPLuu+5nsJRMkZEj3XC+kycfemzbNneriHx7rFhwN8YklLffdmkWr4G5SRPXQ58yxY33DuWffSkRWHA3xiSMn35yw/ledJG71N+r0aNh+3Z46y23XJGVMrFiwd0YkzBmz3aDgvkPFObF6ae7fPvzvsFRcnNdeWLz5pFvY7yw4G6MSRgzZ0KzZpAZtFbkcFWquNEiP//c1cevWuVSO1WSOAIm8aEZY5LJr7/C+++HnpIpMmIEVK/uRoss79R6icCCuzEmIcyd606Ieq2SKalhQ7jsMnjtNTdiZLSHHYg1C+7GmITw9tsuQPfqFf42Ro+GX35xFy9Zz90YY2Js/353MnXgQFezHq7u3aFjR3ffgrsxxsTYvHmwc2foVTIlicDYsS4l43+1ajKy4G6MiXszZ0Lt2m7WpfIaMsSdUK1evfzbimcW3I0xca2wEN55xw3fm5YW69YkDgvuxpi49uWXsHVr+FUylZUFd2NMXJs5002bN2BArFuSWCy4G2PilqorgTzzTKhbN9atSSwW3I0xcSsnB777zlIy4bDgboyJS19+6Uof09JcfbsJjQV3Y0xcKSiAceOgZ084eBA+/BCOPjrWrUo85bjWyxhjImv9erjySvjiCxg+HJ55BurVi3WrEpMFd2NMzKnC66/DzTe7YXjfeAOGljl7swnG0jLGmJj6+WcXyK++Grp0geXLLbBHggV3Y0zMfPwxdOrkpr/785/ho4+gRYtYtyo5eAruItJfRFaLyFoRGRPg+eYiMk9ElopIjojY5QbGmFLt3w933w39+rlqmC++cMspKbFuWfIImnMXkRTgWeAsIA9YLCKzVHWl32r3AjNU9XkRaQfMAVpGob3GmAS3ezecfTYsWADXXw9PPOEGBTOR5eWEaldgraquBxCR6cAgwD+4K1B0/Vg9YHMkG2mMSQ4HD7oqmIULYfp0uPzyWLcoeXkJ7k2B7/2W84DTSqxzP/BvEbkFqAUEHJhTREYBowCaJ/O048aYgO6+G959F556ygJ7tHnJuQeailZLLA8FXlHVZsAA4HUROWLbqjpZVTNVNbNx48aht9YYk7BeeQUeewxuvBFuuSXWrUl+XoJ7HnC833Izjky7jARmAKjqAiANaBSJBhpjEt/8+TBqlBsA7Kmn3IxIJrq8BPfFwEki0kpEqgFDgFkl1tkEnAEgIm1xwT0/kg01xiSmdevcwF8nnAD/+Aekpsa6RZVD0OCuqgXAzcBcIBdXFbNCRMaJSNFwPncC14vIcmAaMEJVS6ZujDGVzI4dcP757v4//wn168e2PZWJp+EHVHUOrrzR/7H7/O6vBHpEtmnGmFgrLIR77nEDd40eDTVren9tQQFcdpnruX/wAZx4YvTaaY5kV6gaY0p1553w6KPuZ+vWMHEi7N3r7bW33eaC+osvQp8+0W2nOZIFd2NMQE895W533AGffgrt28Ptt7vc+dNPw759pb/2mWfguefg//4Prrmm4tpsDrHgbow5wrvvukA+eDA8/rgbW/0//4FPPoHf/Q5uvdWlWZ57Dn777fDXvv++67UPGgQPPxyb9hsL7saYEhYvdqMydu3qhuGt4hclevd2g3199BG0agU33QQnnQQvvODGi1m50l2clJ4OU6ce/lpTsSRWRS2ZmZmanZ0dk30bYwL77jvo1g1q1XLT3JU1A5Kq683fd58bJ6boovP9+90HRLNmFdPmykZElqhqZrD17HPVGAO4cdXPO88F5zlzgk9tJ+IuSvr8c5eKadIEtm+HWbMssMcDm4nJGMP+/S6/vnatq3Bp08b7a0XgnHPcSI/79kGNGtFrp/HOeu7GJIB9+1waJBpU4brrXC59ypTwyxZFLLDHk4TquWdluQsqNm1y+b3x42HYsFi3ypjoKSx0pYiTJrlJLY455tDt2GMDL7dqFdol/g884E6cPvig/T8lk4QJ7llZbuChPXvc8saNbhnsD9Ikp99+gyuvdOOxXHWVy4Fv3Qo//OD+/hctgvx8N0a6v5o1oXt3V9nSqxecdlrpV5a++qoL7tdc4zpOJnkkTLVMy5buD7qkFi1gw4aINcuYuLBzpxtsa948mDDBXSEaSGEhbNt2KOhv2QLZ2e6io5wcl3JJTYXMzEPBvkcPN8bLf/4D/ftD377uBKoN6JUYvFbLJExwr1IlcM5R5MieizGJbMsWOPdcWLHCjYEe7jfTHTtcJcunn7ohd7Oz4cAB9z+Tnu7KHo8/3q1Tr15ED8FEkdfgnjBpmebNA/fcbUInk0y+/dZVnuTnw+zZ7n646td3pY3nneeW9+xxqZz5813AF3FXolpgT04JE9zHjz885w5QtarLFxqTDBYvhgEDXNCdNw9OPTWy269Z06Vg+vaN7HZNfEqYUshhw2DyZJdjB6hTxw0p+re/uVxjSVlZLk9fpYr7mZVVka01JjRz58Lpp7u/688/j3xgN5VPwgR3cAF+wwaXe9+1ywXsJUsgI8P9QxQpqqzZuNGtW1RZYwHexKOpU92EFiedBF984X4aU14JFdxLuuIKN/5FrVruq+akSS6Y33PP4ekbcMtW6mXizV/+4sode/VyIy4ee2ysW2SSRcLk3EvTsaOrArj6ajfM6MKFgU+8grv4yZh4cOCAG+t84kQ3W9Frr0H16rFulUkmCd1zL1KvHrz9Nvz5zzB9eun1upGorNm40Q2wZEy41q1z46NPnOjGRZ82zQK7ibykCO7gTpzefbcbnS7QP0rNmq7iJlxr1rivz61buws6lJScAAASoElEQVRC7MIpE47XX4fOnV3J44wZbqYjG/PcREPS/VmddZa7+KNVq0OPHXOMm0wgnItB1q2DESOgbVt46y244QbXc+/d2wV8k7gmTHC14Nde68Yjj+b1fDt3wvDhbhiBLl1g+XK49NLo7c8YVDUmt1NOOUWjae9e1dtvV61dWxVUmzdXvece1VWrvL1+/XrVa69VTUlRTUtz29qyxT23bJlqo0aqxx6r+s030TsGEz1PP+3+Lrp0Ua1Vy91v31514kTVbdsiu68vvlBt1cr9LY0bp1pQENntm8oFyFYPMdZTIAb6A6uBtcCYUta5DFgJrADeCLbNaAf3Ir/+qvrGG6r9+6tWqeKOuGtX98+dn3/k+hs2qF5/vWrVqqrVq6veeqvq5s1HrrdihWqTJqoNG6p+9VX0j8NEzssvu7+DgQNV9+9X3bVLdfJk93cB7n0fOlT1o49UDx4Mfz8FBaoPPuiCesuWqp9/HrljMJVXxII7kAKsA1oD1YDlQLsS65wELAUa+JaPDrbdigru/jZvVv3LX1Q7dXJHXrWqakaG64WD6+WnpKhWq6Z6002qeXllb2/NGveNoF491QULKuYYTPlMn+4+5M86y327K2n5ctWbb1atX9/9TZx4ourDDx/61ubVpk2qvXu7bQwdqrpjR2Tab4zX4B504DAR6Q7cr6rn+Jbv9qVzHvZb5zHgW1V9yWs6KNZzqObkuBOw7713eK61alVXe3zrrd62s3EjnHGGG5Vv9uzwJzqItEWL3HGdemr8nrD74Qd37cG6da7CKdCtWrVD9xs3dhMyH3VUePubNQsuvtgNh/v++6UPgwuwd687x/LSS67+vGpV+N3vXBsaNTr8Z8nHFixwF80VFMBzz7lcu0h4bTampIiNCikilwD9VfU63/KVwGmqerPfOu8A3wI9cD39+1X1/QDbGgWMAmjevPkpG0srSK8gkRpGePNmN5fkhg3wzjtuurFYyctzkzv84x9uuUkTuPBCd+vb1wXLWFN1M/7ceacLol27ukB44MDht/37D1/eudMFzyeecCfHQwmY//43XHCBq1T54AOoW9f7a7/91o17vmqVG143P9/dtm8v/SRs167wxhtwwgne92OMF16Du5e0zKXAS37LVwJPl1hnNjATSAVaAXlA/bK2G4u0TEki7mtzyZtI6NvautWle6pVU3333ci3NZj9+1UnTHAnB9PSXK536lTViy9WrVnTHVe9eqrDhqm++abq7t0V30ZV1bVrVfv1c+3p3Vt19Wrvr1269FBe/IwzvL/2k09Ua9RQTU9X3b49vHYHUlDgztusXKk6f77qW2+pvvCCy+nv3x+5/Rjjjwjm3LsDc/2W7wbuLrHOC8AIv+X/AKeWtd14CO4tWgQO7i1ahLe97dtVTz3V5fL//vfS1zt4UPXHH1UXLnTrPfqoO+m7a1d4+50/X7VDB9f28893lT7+9uxxHzgjRqgedZRbLy3NnVCcMiXy1SGBHDig+vjjLsjWreuCYGFh6NspKFB97jn3QVW9uur996vu21f6+gsXqtapo9qmjfsANibRRTK4VwXW+3rkRSdU25dYpz/wqu9+I+B7oGFZ242H4D516qFebdGtZk33eLh27lTt2dOdtJs0SfXtt91J3JtvdoG3fftDpXclb2lpqhddpDptmuovvwTf19atqldddegDycs3hgMHXBXILbeoNmt26MTyJZeofvBBeAE3mKVLVU85xe1r0KDgJ6q92LJFdcgQt83f/c4dU0nLl6s2aODKECOxT2PiQcSCu9sWA3A59XXAPb7HxgEDffcFeAJXCvk1MCTYNuMhuKu6QN6ihUvFtGhRdmD3uu7u3S5t4B+469RxaYFBg1R//3tXT/3uu6o5Oa6S4tNPXcBt0uRQoB88OHCgLyhQffZZV9GRmqo6dqwr+QzVwYOqixe7Gv6iHv0JJ6g+8khkerl796refberQDr6aNUZM8pXWhjI+++rtm7t2n7lle4bkapqbq5q48buA6zkNxljEllEg3s0bvES3L0KtZe/b5/q3Lmq2dkuXeM1qBUWujRLyUB/8cWujG/+/EO94H79XBCLhL17VbOyVPv0cdtOTVW99FLVDz8Mrzf/ySeuRw2q11wT2Vx3SXv2uAvUUlNdT33CBNXjjnMfKF4vWjMmUXgN7gkzh2qsxWKC7sJCN079P/4Bb755aFKSJk1cxcjll0enxG7VKjcxyquvwk8/wYknwvXXu2EYjj7aDZ+cl3f47fvvD1/ets0NATF5sqskqgi5uTB6tJtG7qij4OOP3aihxiSTpJsgO9ZiPUF3UaBfudKNYx9KKV+49u1ztd6TJ7uAmZrqZgr66acj123YEJo1c7fjj3c14aNGubH2K5KqK0dt08aNB2RMsrHgHmGh9NyzstzFOZs2uWGGx48Pfwb7eLFqlatN3737UBAvCuRNm0KNGrFuoTGVg9fgnvCTdVSUQBN0BxpGuGiKv6L1iqb4g8QO8G3awKOPxroVxhiv4vTC9PjjP0G3iPs5efKRAdum+DPGxAML7iEomqD74EH3M1BPvLSp/Ep7PCvLpXyqVHE/bRJvY0wkWHCPsNKm8gv0eFEKZ+NGdyKwKIVjAd4YU14W3CNs/PgjRxssbYo/S+EYY6LFgnuEec3NQ2gpHEvfGGNCYdUyUTBsmLfKmObNA5dXlkzhJGsFjjEmeqznHkNeUziWvjHGhMqCewx5TeGEWoFjjDGWlokxLykcr+kbY4wpYj33BBBKBY4xxoAF94QQSgUOWGWNMcbSMgnDawWOVdYYY8B67kknlMoa6+Ebk7ys555kvFbWWA/fmORmPfck43VsG6udNya5WXBPMl4ra2z0SmOSmwX3JOO1ssZGrzQmuXkK7iLSX0RWi8haERlTxnqXiIiKSNApoEz0eBl33kavNCa5BQ3uIpICPAucC7QDhopIuwDr1QFuBRZGupEm8qI1eqUxJj546bl3Bdaq6npV3Q9MBwYFWO9B4DFgXwTbZ6LISw8fQkvhGGPig5fg3hT43m85z/dYMRHpAhyvqrMj2DYTJ2z4A2MSj5fgLgEe0+InRaoATwJ3Bt2QyCgRyRaR7Pz8fO+tNDEVSgrHqmqMiQ+iqmWvINIduF9Vz/Et3w2gqg/7lusB64DdvpccC/wEDFTV7NK2m5mZqdnZpT5tElDJC6PA9fDLGgfHGBMaEVmiqkGLVrz03BcDJ4lIKxGpBgwBZhU9qao7VbWRqrZU1ZbAlwQJ7CY5WVWNMfEjaHBX1QLgZmAukAvMUNUVIjJORAZGu4EmcdiFUcbED09jy6jqHGBOicfuK2XdvuVvlklEoUwqYmPbGBNddoWqiRi7MMqY+GHB3USMXRhlTPyw4G4iKhoXRllu3pjQWXA3MeE1hWODlhkTHgvuJia8pnAsN29MeCy4m5jxksKx8kpjwmPB3cQ1G3femPBYcDdxzcorjQmPBXcT16y80pjweLpC1ZhYGjbM21WroVwha0yys567SRqhpHDsxKtJdhbcTdLwmsKxE6+mMrDgbpKKl/LKUE+8Wi/fJCLLuZtKJ5QTrzZ6pUlU1nM3lU4otfNWXmkSlQV3U+mEcuI11F6+pW9MvLDgbiqdUGrnvfby7SStiTdBJ8iOFpsg2yQCr5N+t2wZuMa+RQt3YteYSInkBNnGVFpee/l2dayJNxbcjQnCS3llKCdpwfLzJvosuBsTAaFeHWv5eRNtFtyNiYBQTtJaeaWpCJ6Cu4j0F5HVIrJWRMYEeP4OEVkpIjki8h8RaRH5phoT37zOH2vllaYiBA3uIpICPAucC7QDhopIuxKrLQUyVTUdeBN4LNINNSZZWHmlqQheeu5dgbWqul5V9wPTgUH+K6jqPFUt+qL5JdAsss00Jnl4zc9b+saUh5fg3hT43m85z/dYaUYC7wV6QkRGiUi2iGTn5+d7b6UxScTKK01F8BLcJcBjAa98EpHhQCbweKDnVXWyqmaqambjxo29t9KYJGPllSbavAT3POB4v+VmwOaSK4nImcA9wEBV/S0yzTOm8opWeaV9CFQOXoL7YuAkEWklItWAIcAs/xVEpAvwIi6w/xj5ZhpT+USjvNJO0lYensaWEZEBwEQgBXhZVceLyDggW1VniciHQEdgi+8lm1R1YFnbtLFljImcKlVcsC5JxKV+itgYOInP69gynibrUNU5wJwSj93nd//MkFtojIkYr5OD20naysOuUDUmCXjNz4d6ktYkLgvuxiQBr/n5UE7Sgp18TWQW3I1JEl7KK0M5SWsVOInNJuswxgTk9eSr1wlNTGTYZB3GmHLxevLVhkmITxbcjTEBeT35GmoFjqVwKoYFd2NMQNGowLGLqCqOBXdjTEDRqMCxFE7FsROqxphyy8pyAXrTJtdjHz8+8MlUr1fSmtLZCVVjTIXxOgtVqCkcr7l5y+MfyYK7MabCeE3hhFpjb/X4R7K0jDGmQnlJ4YQywFllq8f3mpax4G6MiTuh5OajNSKm1/MIFc1y7saYhBVKbj4a9fjJULJpwd0YE3dCKa+MRj1+KCWb8ZrHt+BujIk7oQxwFo16fK+9/FB7+BX5QWA5d2NMpeE1j+41Px9KHj9SJ3TthKoxxoTJayAO5cRvpKY4tBOqxhgTJq+pnlDy+BU9xaEFd2OMCcDLVbeh5PEreopDC+7GGBOmUE78hjrFYXlVjc5mjTGmchg2zNsJ0aJ1KurCKE89dxHpLyKrRWStiIwJ8Hx1Efm77/mFItIy0g01xphE53WAtUgIGtxFJAV4FjgXaAcMFZF2JVYbCfysqicCTwKPRrqhxhhjvPPSc+8KrFXV9aq6H5gODCqxziDgVd/9N4EzREQi10xjjDGh8BLcmwLf+y3n+R4LuI6qFgA7gYYlNyQio0QkW0Sy8/Pzw2uxMcaYoLwE90A98JJl+17WQVUnq2qmqmY2btzYS/uMMcaEwUtwzwOO91tuBmwubR0RqQrUA36KRAONMcaEzksp5GLgJBFpBfwXGAJcUWKdWcDVwALgEuAjDTKuwZIlS7aJSMmLcRsB27w0PEEk2/FA8h1Tsh0PJN8xJdvxQPmOqYWXlYIGd1UtEJGbgblACvCyqq4QkXFAtqrOAv4GvC4ia3E99iEetntEXkZEsr2MmZAoku14IPmOKdmOB5LvmJLteKBijsnTRUyqOgeYU+Kx+/zu7wMujWzTjDHGhMuGHzDGmCQUb8F9cqwbEGHJdjyQfMeUbMcDyXdMyXY8UAHHFLPx3I0xxkRPvPXcjTHGRIAFd2OMSUJxEdyDjTqZiERkg4h8LSLLRCQh5xMUkZdF5EcR+cbvsaNE5AMRWeP72SCWbQxFKcdzv4j81/c+LRORAbFsYyhE5HgRmSciuSKyQkRu8z2eyO9RaceUkO+TiKSJyCIRWe47ngd8j7fyjaC7xjeibrWI7zvWOXffqJPfAmfhrnRdDAxV1ZUxbVg5icgGIFNVE/biCxHpDewGXlPVDr7HHgN+UtVHfB/EDVT1rli206tSjud+YLeqTohl28IhIk2AJqr6lYjUAZYAFwIjSNz3qLRjuowEfJ98AyjWUtXdIpIKfAbcBtwBvK2q00XkBWC5qj4fyX3HQ8/dy6iTJgZUdT5HDiPhPwLoq7h/vIRQyvEkLFXdoqpf+e7/AuTiBvFL5PeotGNKSOrs9i2m+m4K9MONoAtReo/iIbh7GXUyESnwbxFZIiKjYt2YCDpGVbeA+0cEjo5xeyLhZhHJ8aVtEiaF4c83QU4XYCFJ8h6VOCZI0PdJRFJEZBnwI/ABsA7Y4RtBF6IU8+IhuHsaUTIB9VDVDNwkJzf5UgIm/jwPnAB0BrYAf4ltc0InIrWBt4Dfq+quWLcnEgIcU8K+T6paqKqdcYMudgXaBlot0vuNh+DuZdTJhKOqm30/fwRm4t7UZLDVlxctyo/+GOP2lIuqbvX98x0E/kqCvU++PO5bQJaqvu17OKHfo0DHlOjvE4Cq7gA+BroB9X0j6EKUYl48BPfiUSd9Z4yH4EaZTFgiUst3MggRqQWcDXxT9qsSRtEIoPh+vhvDtpRbURD0uYgEep98J+v+BuSq6hN+TyXse1TaMSXq+yQijUWkvu9+DeBM3HmEebgRdCFK71HMq2UAfGVNEzk06uT4GDepXESkNa63Dm5wtjcS8ZhEZBrQFzc86VbgT8A7wAygObAJuFRVE+IkZSnH0xf3VV+BDcANRfnqeCciPYFPga+Bg76Hx+Jy1In6HpV2TENJwPdJRNJxJ0xTcJ3pGao6zhcjpgNHAUuB4ar6W0T3HQ/B3RhjTGTFQ1rGGGNMhFlwN8aYJGTB3RhjkpAFd2OMSUIW3I0xJglZcDfGmCRkwd0YY5LQ/wdyhKRUNmLO3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(1, len(acc) + 1)\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用数据增强"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(\n",
    "    rotation_range=40, \n",
    "    width_shift_range=0.2, \n",
    "    height_shift_range=0.2, \n",
    "    shear_range=0.2, \n",
    "    zoom_range=0.2, \n",
    "    horizontal_flip=True, \n",
    "    fill_mode='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0ZBIo9a7ucQrO5bTLONbBqCCR32vbQtVwR3ACmXccCh6hthty0XDLVc3hcQxghnHCeCSumPGMTEIpYK6QWpxkFsqmYlHHoOXYZnzjbixpvzWbEcQVq60mbQ2qFu1qVO+mBGXBdBpZsV5JhiTLdrRdVVXbiL9JUOJmZPmYogvoek8qGrBuqt6st9ZK+Xq0zowxZFmX5UipxSKs67oPRu5kKPTzGYbht94VCC/OBAwzBtdxAjfV41yXoRUw/Pwi6dynoUXwCVu/4PPLySuxKCi2SDStdV8U39QNVjIBXgnoCEgCtIL1b6sVYbXk4sexKvTR++/zUFyG9eqUE0HhWWPpcmLlxnLVxRrshspWfUFOQoGqO8AQVBI9HycjgrxUpbOUkt40tkVXG7RYhUU2xYjJ6+uGibyUrRqx2sRJ6IIlL3KsEzCSb0mn8Tzzo+gjA7RNwEna0KkRqyqeS2UtSWLIxQ9fLZZM07h/2bp+8rrWUF1ISjFJcFI0tSrWkBh8hwj0ijbEiZilBScC5Ll3OCeRl69tGpQc43B6B5UWA2BRwEi9SkBDSPtxgxnUNAw4C7xWPWDNJCmZAJ68D71b0aFVAQ4Pj0hk4dxsNlhrd+pfjo6O5LNhuYwLUV3XW/Nbqz5t3XFbXM8MxOP7G1GQ3Tad+V+WZb/4vEiupwSH7syL5Hoc4PNM/uv1I59VXolFARRBfGLblthKLIXWojukm/eUUklocXzwfsQVbJZX1MslV48iH8DF+TMqiS/oRFO28XNAU4t1UNaB4CTg4xt83VKthLfBGcZ5fPldCCjhBblabwiNrOZaMx+L35qBLa9wgjkwPqO6iuc/LyYEeYlscCQ6HlOH6LclKk4e6xrWS0lXaktQwvUwylGFoA6zUX9ddRM4nI5pmzh58vmEpZzaCkO1qeT4B2iZlD6Blni8elOSjky/yB5Oj8DHv82Nwsg9v51NkIJRrNVM5L74qiJNdc91oRKDlgVDJZMe2owG3S0Wre1h3kmWoRPTT+r5wYjDgwjZHhY0XUchdjgTpRR5nl+DNMshjSETPomy3uAE6VrV5Y4GL7LRTpFUX0E5sACGwcDu96F10MlnSQkOA5IvSrFf2+PGz9e3/6ILwVD2MYW97GUvO/JKWAohQBeUtR6qzo9sKxKpLsqbmurhB0CsCTh7N9YhPH7ymLZtqYStyDuHsIyxahy11AckSlPXgig0ObpDyvnANBlRSepvPBrRSiTb6Zq33og+9cXTBeUmnuQkH9OuhH+g8SSFZiWWR1W1pFYi3psWxH3wzrKSuMV4MsfolHLTMTkVvdZbXy1IC9GUiaaREu+2bUl0dEtMMmKzqXotGHCk4mZNlMJI6fQ0S/sHvFpekEyiZnSJYpTl1BvhfVhZZpIlmE1H5IVYF63Bdy5bNu1NfFxLvbxA5/E6i/kEJf6TNzWIdaBd6OtASMYo3cVNHJPpFKn1Is8ytNlq2k6um9pdVmA8mUBQPe9GVdeDOEBk0wK4fft2//tisegzFNGVUb0rMKyjuG6uX081DvkVPi0O8GW6Al9mY+hXYlGAgA9xUibKkQpOwBnXuwznH73Px9/7LgCrqyVL4QJo6xobApu1LAoo6jq+7PW6jUg6IMsNSnzSdbXC9sUwCqUK5sIfGHxNPhIyknDJex9FOrOj8WtkI9l/s8RK2q4IBYXJyVR0GYrpmFEh6cWzK1pBMeaTDF1v/euqbPDd4oGirbuKyQQn0OrRNCcbx/NqSoeV66qCJ5SOTN7JPEtorVxzcPiVuExGYcbxmFcsOBjF6kWTT3CjEUpJlWTlMXI9SW379CIuDJCia04fxXtsCsPoaMo8i4HOxgaQoGXmx0jogqBSJqOIovRG91WJaZZhEtMvCkrr3k2MfI1bYpxd6bYxuODYrKP7ZBJDLoHWqqqopbI1y7Idt2A4Kcuy7IONw5jCJxUtDd2EJEl2XInP7grsfv+yJ/9nGWbvPuxlL3vZkVfCUgjBsVzHQKEvS8qLaB1cPPmA0w/eBeDjD97lXFBrrvWYjYCCmoa0GLNZR01VNTVBwszZJiWINrAO1pJqU4nHC32Y01AHTybrY1EY9EjSW7xG23ZpME0lnAsB36+mbbkhtDUdQZQa51ipI0hHGVebaGlYr8jHUZuNi4LlsqIRROE0nyEKGesMK9H0m0nLwVyCYd7jhd4pSyYsywVIQZHXmtk8ph4vl1e4PKqFs3XN2MRjzg9PCFZqJ+qGiyZgUsksVCWVsDI1TCh8POYoT8hcLB3XyYjjw2gZ3Dm+y6qxPWBqs1jRCO9CpjPaukNR5qyPIjpxfidnMosXmWcZNmwZsdM06+8fL4PXV8K0PUC+DlOCmzJakSEEErMNWuqePVrvWAQdmKrbblho1P0+DH52x3kxEOmPzhWIQw0tre5+vnwdxCuxKHjX8uyjCDtenj7m/L2YXrx4+oyri4hOXK43WLFrF2VDGuc363UFukJLQU2Wmj6lV9ULemPIG2qBRqs60NvIOmFmFJXArNsswbSCB2g9WiDDSgMSNzg5PqYS0zVLU66WVyi5lc46EJO/sjVe3vb81hjJLqIUzI9TxpLNKPSEch0RlkVm0L7DbGjatcQU1oH6SmDJ6yvyIiPLYhrRqITVpSx4LaQyqU1ImFVxrFGhKaUydFNdgU2xgqIM1qLHcYF4drnktTtxIl8tW2bC0uwWFV74HtvFE5IiZyUEDda1SN0ULk3REtXPDrLtHFcDJKE2GJX0xU5JkqK6as6w5ejcIUlB4XzHkm2xrd2JCYTefLfbgiyd9mlT57dZheihbNOF2QBDcT3NeH3iDxeM7triOH+Uk3844YfoTG50hT5N9u7DXvaylx15JSyFarXiR7/9mwC8/8GPWPWYAdgs4ueiGNHUXSYhIRSiAdMx6+UlHRtz3da0UvikUo1OpI4gUcyEUWnzbIOX3gRL5yhGikaKkC4eX3J8q6Oc17SSCWm06wFOZ5cXpALw8Sha7VmvJLOgMzKhU9s0a5pO81yOsOKKJNPYEyIITqGsttrJuRKtogZdPbUcybno4DBZ1FLz0RjnaxrJ29urilIsl/lkwnwWr+1odojUcLFqN1xJMDbYlqNJjhfrqgoNVuo6DiZT1pt4/xKd0go3gk5S2i5C39QkmeoJdk2qoZTahRCwYnW55JzJieAX9O0t2EhpVGL6bEAMPIoV4t22B8e14qJOS7fO4bza9qdwHm+FhFUp0g4IZ9IdPMF1Qteb6gWGZLHXXYyIkxhq+y5Q6b/04GDomyQ9X5T1HAmu7gBbus/4vJRbJvK5FwWl1BvA/wjcI1an/GoI4b9VSh0D/zPwNvAe8G+EEC4+aazNasnv/uZvAFCFllrMVNVqCjHfHTWppCeD39A48fHUhGycbDMWPlA3Yj7rlFzQR5e2ZiUm+kE+J4ivvaobHl9cgVRJxpdTzGpve8i1SjV0lF1GYTtzdVNhdcZaYMJJG7DLGEcY5QmEOCt9o8jTuChZ6zi9WOJkkZmMUw7ETNfG965I7se0QRiTVYOVKsdiNKLdQCIT9mA0JwhIq/KXBEmd/vw77/A9ydhcVku8pA1H44zNquxf/uP5EVrGKvJR/zKGAGUZr3+dnGGF2dmEAxgd01zJ5Pcwm8b7ub7ypFJEdnQ0YiZAopS2Rz0mSYIypm9Os16vqOQ+ozS6jwO52I2K57MCHSwewLb0rNuJ0aSm29+/kIfgOoqxk+HcuanQqZ+IL0Hq8rLySXGA665A/30w4ZU228k/WDQCauta/CG5Dxb4D0MI7wC/BPw7SqlvA38D+LUQwjeAX5Pve9nLXv5/Ip/bUgghPAIeyeelUuq7xB6SfxH4FdnsbwG/Dvz1Txqrto53T6PJauuasWh33ShWrWjHDE5uxej3QZJQl1HrXRUVwTWcnsco+WQ6RXXcBs6iKlnpnQaiBaFSQ5JGzTgvRpyHDaEWTV+kWInE57nBiNbNg+GyY3yuGw5nh7L9lHVT8/rt2AClWi1o6TAHCidWR7up0XlcrU0BkzSlFGyB8jWJ9IBAWWp7LtsdMJKg4dPTcybTDma8YaInXJxH6+BceyqxVLIiUEk7uh+9+33KlXBLpIYg2YraWSaHh4zFtTJt2pvs66bEyL05OjkhiIZ5/fZdrs7E4EtGNDahEZBU7eBqFc85TwqOx3JvVEZ1Fa0zp1MQpqlTDEmWxDoJwJicpOtVYVwPs45/i+cyZFmK98n0Fo0io0/feEddi3VlXX8MtWsCoHzAiBb1asvhEf8c+n1uYlH6PNJbXzdmA9SNroAatkrsLAO1tQiGYwX1vFsxDDR+huTDTyamoJR6G/jjwG8Cd2XBIITwSCl151P3DxqzEgKSYJB6HmwomAuv4LgIfZ0/k5xVV+iyrMA6ZkUk/DCm6OMLTbPu6dTa2qMEqdf4Ei3mamsC6UTTCiKwdR6lhAuxVHjpCjWe5FyIT14kKY2Mm2rNyHuSpWQjdIMRROJm7aWZKkwPCpCX1Tcts0yRdj69W2NdvM47J8d88F6c1OoqcHQ/FvokBwmJZExWyzWTYkoyiftcAK2P17O4vGAmk/2ji2dkMtlSkzOZR/fFJQ6nNY8l9TufjFCSxiyXJYdJPOamMrQujvt0ZXrq+mQWSMyIRAhYnHYgMRpQLKSgqXrYkF3GxWJ+t2L6ugCk0kBhx6RGnu246FOHKtlOjEib9nznpmj6W5zrvvv+mQdnsV1MIgSUGdZOSEyDyKRtlO73D4NF4UULwbDh8NBlGLoVn5YSvKlpzI4roK7HAbYZhsAW2HX9PHcm/+CQn4eR7QtnH5RSU+B/A/79EMLiM+z315RSv62U+u3avjwn/V72spcvV76QpaCUSokLwv8UQvjf5ecnSqn7YiXcB57etG8I4VeBXwU4mU3DTHL7SZZxJoxCB1aTdevFKKWScunq9AIpOCRXIUafxeWo164nK9UeUjGZRyHDCZ+AcjVVJfBdbUhmCiVt02zjkIA9XgeqiWjnCUyyCNkdB00+ipox+IBrLRLnpMjGrCU4qJMEJDjnfMtIKh6rNSSFwqro8iT5mI2URS9XjtlBhCP7jeZsI0CmoHoLV49SEtUwluCe27Skbbz+Uh9QiAVBMWF1GoOe41z1ZdD3br1NvQndqaFSw3waLa17hzmtZB9MaEnELXHovq/koimx9RWhja7BaDQlFcyBbwMLgXarSnGwkXqL6ikbwT/fLuZk06Svq7C+wQm9nVGq45CFsK2kvE5u6n3YugbBo4bmf0/tvf3VhYGLIFXcHT9DGGjT67RvQ9FaD9iWoFfJSg/4GV6AE9AapXbdAfRncwWG3z/JIvi8hK2dfJHsgwL+e+C7IYT/avCn/wP4K8B/If//nU89CaN5+8F9AC43K5QRTH3V4LrimkwxT+JETFuLlxRabSzpOKORtuy0Gld2Kb4WBJ//xF3xIJOF4HJDsNGUnswmbOoVVddMxTmqZZyss4M5Y5lgYZQzlpRW0tSojjwlQH3R9KCXqZ4ykgXOu6qndqs3gSSJi0qjNInOyTIhQDEJwcelaL1oSaWZpVeAFAot2ro3he8cjsj8mlyyIR8/fchYxes5ODrkYhPdgmbZsBCXJT8y/SQ4ffgQ1U5AwEt5MSURajVfe3LJzGSJ619Q6z2tnEtIINCSSeXZ1WJBImnUHMPrb7wFwNPTU5pK6jBaxaKKi0hSzBh/NUcJa3TbrDuiZzKr+5iCV8mOy9BJ91lfmzBwbUIo1ZO2DaMGQXUJvkGa5QbAlHWuDwYE1dHMS4Gbdzs9S7dZCTPICjzvCoSBO/BFXIHPOvE/y/ZfxFL4ZeAvA7+nlPpd+e0/IS4G/4tS6q8CHwD/+qcNNJ2M+emvxc71y82KJxexG/LjxVOmk/iyjlxCEL5DM0552KH7ak/uQ8fKTmXPCSa+iL5VBB814CQpWC6koEflOBlrdbqEtO3vuEkKllLcY0LN0STiBHKTY+QYT9anIIvCLC+4m+VYCRS+f3pJEQ+PMo75cfTPz+s1lWhKnRu8VniBCdebElxXwZlQjOOL2NRXHGSxH0NLxkbo6m/PjjieHfLsaTzQ/PAIKyjMdWM5OHw7Xr9vuJNLTCFT5F23JzOm3qQURYydjGYps1lcVJrNmkwWhZM1eUDHAAAgAElEQVTjOafn0dAzuWYtwd08MYxHBWOBKVtlkdADtql59NGP43HGY3Lhrkycp5TK0osPP2ZWTDi4L2lErWhl8lhtyLtGwOyyHV3X4OYGUlatdT+RYjFkN6lV34WKAMH7vqM4gcgtCSgz8KiV6lOiwcf1uWdLMqYP+hljtl3Mld7V+oOx4n9f/uS/TpD7Wcf5ItmH3+DFMc0/+3nH3cte9vJHK68EojFNDF+7fxuAqsp541YXPZ5QPova8GoNZ4uowVNbcCjmstZgw4Y87xqZjnBtXB3rdcNGgERXqwoliMCiyHC2Yz0yNFWLMoLoO7pPPhN04+acXIBQIW1QQs2WMqKRFGbbwuWyZZRFS2Ga5hjd4eM9jx7GQq90MqcRa6DINco3dDXG7aZknHcUbgEn2qowt3DClpSkcCLZg9lowkQD0TPi/PSUq0U8ZlJMsOLTaxpeO4iWzsnskFvH0iOz2XCWBpzEWy7cM6zwTqRZyrFQmzkDR/fvyT4V9yTB4Kqa+nKB6mjlk7xnVSoOClZNx3BV40fRUjsuZiTCDXF5esZY/5DNZXTTXvv6N/DCN8l4GtFQRJo2+gzBbhZAKdUDlkIIW64GwhYJOFRZRm9/CCoCpMRyCM6jxM0zA1cgWEtXI6ONIU0ydA+a2roCsYHRNuPwidbArnfz/HafIjeNfR3YNUxvqi5t+xmyqa/EouBCIAiHwFExIiG+vE8uF7QS3MuCZi74gU3TkMvDKjws64aNxBiK5ARkIhVZYHQUffVxMWVj4wKzKpdYIWEtNy2trzHjON7l2RWTWmjFXcMqkaDbdNR3mrbWUa7jS3TBmoxYaQix25LtCm0mOYV0i2qtYb2I6bm7J0ccH835wQ8jpZxHkY3i8c+fnNKKyzOezigE81AcJpgsPuxkbDg+POawihNpsbScnr8Xb2ZmMLNops+ygteEePXOKEcL/mC5CoRQsl7HZFE6MbQhXs/hwWHPO+GtJZGCrhEZWqK7ySRF6zFdtLdsGy6kP4Nbg087LslAK30z2rTi1kGMofjlkrNHT3rYt3OB8e0YXD1+8AZmLLEXk2Hy+Pyum8Xdb0Ckse8nn0ZJ5FAF1RdaOa/6fg9KRb7GbjStTU8cq/XWFcmSbBATkPhA1xLvWtryD8MVGBZx7dLSbye9DvQN1nYCnfuCqL3sZS+fV14JS0Ep3ZcYr9c1y3VX3DJBiYlb1Z61FMCsW0cpTEWblcXZjHEu4CXmtG10GRITqIWmLUmm5BIhT/WsjzldmZJajSjbOF7rXE9XPk7GWOl3W7kGjLgvWoOYwms8C51QSNCwqhpmRdTgV5sLbp3EAOrm6YKkO2hqWNYllVhHrmrZrCJa8PBkjpN7cV5dMM3iMY/GYzLhY1DjQGU8Y6kxyNQBR4IirJpLZlIX8ubxPW4dRk07n01ZlfGcD46mJFXG3Mb9D2YZk0NpOqMgEd6GxGoS0RtL56kk6NbYDdZXTCbxntvK98VqpshoRVO3zlELh0PdLCmFBStPU9pNw/KD2M/TP3rE6CiO9Xa14vVvvhPPJZ/jJYCaZdluJkJtzXdNgg5denIIChqY0kb1jXG0bNdpxFheLWPrYRrwButkSBGntn7KT8oi+KTPenBIdc0i6Jmr9PX9P9OpAK/KooBC5iRt5dEmmo+2shC6Zq9VBB4AITGUTh5OdsBqcUEm1Ga5sThB9yVpIJ9J4VO5JoS4KGiVECTanOQpdW0JHUmH93h5edN0QiLdsKu26ok8Ujbg4yQqpnM+enpJLmZykihKSQNOpyPcZTSrTV33MYGMFmUd98THX5yvOZC/qdTg6fo71EyFJm7kW6xQxKfmDhsqnDBA2zYwncVJFcqSqSAN7xzf4o5kP5Q25NKsNg8J09rhZVE6nM0I4k6t1k/7yVfM531bqvr8gquN3DOfMkqnXJ1LWrhtUKbrkNWy6mjQXeirTNM0oVoK5wOB48NbXD6LrpmrWg6E8OXhxz/i1luvybMZkTqJ6bjQR/hDl8vfmTDb9OBNEXejFd0ro5QSRu1BlF729+aazT+silSq/64GaMOXmXfXzfgd338Qxxi6Air0r/wNBVHbCT+c/LuL0+eDZe/dh73sZS878kpYCt5a1ouoUZu26Xsm1k1F1Ui3IhVousYo3pJJLhsVyI4CaQdTsWu0WBfLukJrKYIioxWz2AOV9ExwBprWknR58sqxEhLZ6qLip9/54wCEusQ3MVo+yseoDve4KfnqyYjFclubr8SiMM2IQ6EwK7Tl6FbU2m9/7U1++OPvo6SX5GQ+pusmc/vwFo3gEWYTRUiiW6HtmLcOYyZg7hNc42kERmkmgauzCAwKCh5IcPHB8V1SURZXV0vWgr/QQTEdTzk4iZmJ6WzOk6cRRTq+Ne9bztftGauzeJ9WS0XZSKGZb6mW5ziBp5tBbj9JM9I0BldL25DJ5+Acjdx/Zz1ZEih1x86cU3UsSlXL1WOpyTCHfX2Hd77HJShlYsR/GDgMz2vgITeC0YagB4VOch+giw2q/v6FHW17s968Xhz1Mub/9eCgfoErsLUgnieRHVoEW0vgBRbBjqXw8lbDK7EoBGdpyzjhVk3T3y2V0kOWa6sog3RprleENk5Kk46Yzo9oK2lW2gQwUtA0nrFaRFeiWbTkUqW3bC9Bov0OB6liJA1YmhbqWlpYG8PHj74HwP2TKV/5ejRrR2lGIi/LZllxdnaFEfRU0+q+Ss85h0k7gFLdV0XqfMRr994iI6YrN2FJoaUBTJNzchjjA21ac7mJi0I+GnM8ixH6W3qMSjNWwgVzurjCjKUR7L03eHAnLh6J1WDivXTKcXsWF6WPnzxFJVdspLK0/MgR5N7MjnJSoXtfrBuenseMyapuEewSq7rCZ7qvrNReUTfxj+NszFTu7f2TY7xcc1k1OPERbeV5tPgxd77+jfickhTdCrAspDSb+Mxc0+IEyGWt3RZHJRJN6GaIpm/uM5ys1xuzJJ25L67DTb6/UeAGP19HVL6o6cuwsrL/nedjAEMqyptiH5/JFXjBpO8rMn1s3Qfb/19G9u7DXvaylx15JSwFAmhxBw5GI1bSFHZTVj0LkbUNtZQx10GxqGOGQVGS5EXP9qOzhPMqasDT9TnHh1G7Hs7mmEbM7RI2YiJvKodyUApOV40yurUyU46RaNrDSUYtnAHTw0NmUkCUBIMxMzZldDmaqxot1sHsYIbEKZmGhKNDoV+7ekahU+4L1Vp7OMVKKXemUw6PYtAxn9zGPJNCIwd5Hi2gg/yIj5+dcu+PfR2A1eqSg+Oo3V87uUN7Ea2mf/rRhzwUa+CjJ2d0BlDtakzSUkngsNxo7t2L1/Pg9aKHlltXk0jDl/PVRd9bwruWVN1lLBiCYDRe6iC02TIzJ84SpKBqOappxbJovMPn0EiW6PXDr9KU8Z5N8xknwgCdj2d0TYWzLNtyKlwz6ZVSO9G+F/Ee6OHP6ubtgqNnyR5q/f443VjD36G3HDWqLzG/jhPQ5jruYPv/y7sCA9j3oHRjaxEMLQXfB9S7Rs0vI6/GoqA0SRozDsoo8s4R9obGCx+Cb1m4jnLrgGIsXYonnquzZ6haiD10iTbRFP3qN05IVXzB10/WtNKL8TBPmet4vHOvWfuWbBYn3HK9ppIof5ZrQtuh3mA0E5clSZjK9kFZjm8dk4h//Pg8Z9NRwQfPSBa7k8MDJvMYX7h95y7OOiqpgGxqGAmwaL1uGEll5msPXuNCIvRHh0ecjKP5n43m3J7N0DJJvv7mN3j4JKb3fvDdd/neux8C8OHjUyphsHYuYDu7uBhhVUsiBWaN0vilsEkvrnC1XBuBg5NIHjOf3edAiGUKnVCtK4zEa1xQiPeF05ZEkI63R0dMxS05nJZ8FGKsYLnZkHqNP49xjNH9r5HO4/NYNA0fnUWl8MYtj1BxYq3tJ1RqCmK+QdCG/maD94Vgp4Gp3/0/dAXs88xmz41nBpkBrVQfV3k+jjDc/xMyAp/mCoQ44bvJH0Ks0AXpjt1Xb/q+56ZtW6xwV26bH3267N2HvexlLzvySlgKAdX3ktws1z02YZzn1FLGbNqaTAJ45WaEEi2xOD8jSVSfc3atYypm9qQ9ZLGUTMK66TMBo6Lo284VeUaWj2ikiP/w4KCD3uOdZ7WWasxKcSA4h1E65kDcldtHczZNg74r9QqjjCsZ21nHTPovHkym3HsQgUw6S7i8OMd0mRXn+jz5bDpjLNDoyWTCO9/8FgBtU+NFG11NFemtgplQNV+cb/jH/+iHAPzWP/lODNYCB3duce9BNMXHowlPHgsL0ptHNK7ktbfj+Tz5cMH5x6K18/scSh3H0eEBieA88nFKIfUlzjUcjo6xYkV55ftqxKZtyMWtGE2OMVK6rjljJlrztaKg2LSshJPHZ4H57QiBTqczDu/E7EmaFiRiaaV5ihEXRSkTGaHDVvP3LedfAAW+jl94EcAoEAbcCNushFYRJm1kP6MV2Q1t5YceyecJDl63BrYughd3YEs2a8Wds22z89lLfwzvXN8rowsKv4y8EouC0qHH/rcu0AgfQrlpKYQDQfmUjRBxrLSjvowvfq0OyQ+nKBMvfjYZY110Oc4uHzORQptidMhaGqakNmcupcI6zchHM9aCyLPBkUmHqGfPHnK2iiZv++MVwb0JwL3DE0ZS6JMahZkWhGU8z9OzU9756lfjWFVNLuQjOk85EoBRWW+YJoZCyq9TrdkI63M+mTHNoyltmpzZgYCv2iuc0LdNXzukuDUnrOKCdb56yG/9zj+Lx5nOeedbcbK//e23+eDJRwA8fXqJ77IyyQGejItFdBkevH2XICCjj549ZvogZi++/sYDMikAKjcbgqQUZ8UUMsO50NONJpM+XWjrhnwknbAmCZUQyzTPPIcj4bDQBRvlCJnUNYznTI5jQdytkzcoptKWvhiTdPXe+ro/vjv5+5LmF4CXri8CgdCnHoMK/aI2HNcE3V+X1pqdqmoZZTieDPACF6Ez9en/DztxAMlyeN/7/8EHfOcK2HZ38tt2Z/J3n51zg5hC6GnwPwu/5CuxKBijIIkLQVWtqaVwKc01VnLRq1JRSqGTzz23T+TFmU05rS6ohBfwKJ30qcc6relwyuPxjKnk5TdLy/37URuNRjkksKriC76qahZL6RXhV+SZFP2saj5+GBeIWfY+d0+iBj4+nHJ09wgti5dRrzMbx4Xo8uy8f1mzybgPwI1mM4pkRCOkK+erK2pJw6WtJZVAZZs4zFg0qEtwgp+Y3hnjsqyPl3zw8AkbCSh9++vf5GvfiovSVXPJuVhaT87PMTYuSs0HDzm8pTluBaZ8eoYro9a+e3eCXUcOhTtHP4+XxaJcrPtAp20atM562PbBZELadbtVnnUTF5uL+jGJdPg6tAYruAqrGvRMMx/H8Q7nh5zcibGL0XiMyToqfdOTpGij+25PEFOCmXSiGhYkGWN2YgfDlGK/MGjwZkDXrgYkKUr1xzGoLZGLCrHzVD/Etuv1sO/DEOm4O/GVfO98/7ATB3iR1t/2pnA7k79jn4I4+besVM/T0nefX1b2MYW97GUvO/JKWAoEzSiRdNuk4mLdtVIPKIlwX9YLVqWw8Y5bRpJCvFo+IksTEmn/rq1jJlH1yXHeU8Rb5/p+k29+5S1uSzrw3smMbJyyEf7Hy6sFy5V0SFINP/6B1AGMG64EdbmuGjaymp+McnCOTLTLdDyhkjLqrMhRxOOjFjRJNJ9NXpAGQyqdoSajFISCLfiSq+VjAOapIReW6rqtCdLuvllvyPWEVu7H6eUFPota59H5UzbfFY5EY9GVNLElp/TSIappuJdPSRZd7OYBT34cLYr7v/g2t4XPYjIa8ehxPJeyXpMIG3ZVrwg+42ge0726cSylQ1YTamoXr18ryEfyiuV6G4PQMD04wMjfspEmL4TNO9W4jgUpODq9ZW3b0/IXWU6aprsxgkFdw05fyms8DPG8NNpsYweK0FsHKu4oz8LhBqm85zMJ27LqrqAv+AFbU/B9dMIHj3fu0+MA3vfWmfduxxrwIfQZh+uclT/JPpWvyKLgaRYx0DXTAV3ESb1xNRuJQN46OCBLOvjtFYkUPc1VTk2CRRqholjIzU51Qmo6IhDNeCINWY3HybjLxRVTtSVG+dob97ACM35wfMAbs5jqK2vLo9OIQGxDxYcP3wfg6CgjL47RklIzRUa6kdQphsRI34ppIJPjN0kGScv6TAqcFOjeUG4phOYMX2GstKPzNdZGszxdzEj0nIunXUfogGvidV48c2ikyjTZcHgiky1ReEndldUZRZLz6KGgSFcfMJF0o/Pwta/+QhzrvOXJmbhVZctF+XG8rjQlTQ9YPZY+GOu6LzwbzcYE4ZJMxxPaJMYNbL1hPIluVdU66qbsoeXeGdbriFkodNL3nVDeYUK8rvFkwkjiC6kyaK16fofrk7UnuL0WCBwGHYNzBLnnAXDhhg7SwYP8rpQBbeiNa6X7iaxwPVWcUvRdxZxt+/Sgs7FL9XbyD9vjuUF6cTvxb3IFfpKT/0Wydx/2spe97MirYSloRXoSTev62bZ9fOIVuqvTb1uMrKyJywmiDdOiYFqcsF7HFbR1Dc5KunE8IfRMOTrSawFOV7Q2asBnTyuePG64fz+6E/O8oEij1jsaTfmFP/ZtAMracXERg4vzseH27ahZa2vRpMzvxr+50FILHZyvDUZHrXF5/mPC46hpj157k9b7SAFPbB2vvbSc955GTP6mvcAL4Gg8P4LsSO5XgWJNvYnaNSjHsZRI+2TKWpq8jKeBqor3qbGeIovnPE0P+Gfv1VxKw6exWfPzPxPP/0//yQfMbBz3g/ce8Wwd05iNW1ELpXueFeS5Q3cMV6OUpusT6QxB0D/FeNa7YtMiJxewVZYWOAKpMDwlzoEUqKFu9XSnWkEigdrEaHTXROoTGqsMZbdJy7XekSoAzzeyVYOgo9KaIF3FbNvi7Dbw55yFsNX0HTjIuSFIaAsv7KwBHwbuwA2uwCdRzL+MvLAg64b06YvkCy8KKuJQfxv4OITwF5RSXwH+NnAMfAf4yyEIj9oLxAewHWmKD7iukawJ1GK+tW3oT3c0G4Pruj6XqGCZ5dHlGOcH5Gn0w5e+pRE/1nnTR8+1MSQ6HiOfHXA8P+boVlwIkixQCfLRpHB6+QyAh08XePn94pnjQrgEprM5b+R3sJKuUyONLqLJrMdjEnlxknqJbeMsXF0smU5vkR/GF2757ClF3nE15JHEBQhJoJbsSaImmCK6KE7HjsojcUecdcyn0X1qg0YpoTOrrjh9KP15RgXPpBLSrTz1uuZ4Hq/5Z7/+Br/4M5HY5OTwhPffizRxl/UpuY7HqJcVRiZI9Mhczy5NPkFCGtRn55wI7Vq7WvawP0VCLXwK603NZDphIl3AlYbVpcRhDhxBnn+Sqa0rgNpOKL3LhQDcOKkU+sbFwtkWdy1e0GUMrK1617JtB69t8ATvdsz/fugdk3472b333brRn9dNmYHPI88RsNzQbDZ+fp4+7tPkJ+E+/HvAdwff/0vgv5YGsxfAX/0JHGMve9nLH5J80Q5RrwP/MvCfA/+BisvRPw/8JdnkbwH/KfDffdI43jqefBC1WNAekqiBcpNwIIAfr9ZYqX1Yh5JUEI0H0wPKq6qvHUiDi9VDwDTXhHHURuPRMUHM3aoumUjr94PxmInxPD2LvSYu65Lf/YO4xv3gBz8mEVB/niq03K5M5b2JPJuMeXD/x7zzCxEb8OCbr5EdS3BznJMJBdyRf8C5dIEKjSXTHjsS12iuSaPFTihLmo2s9Lni4E406613FEUXPW9o1yoWTQBfuX+CNLjig4+e4rqahOSYSpqXLKsVR2KKz2+POX7riG9882sAnDy412dpvvcPfoOZMEsfFGO8i/d/entOKY1vS7+ktJZN17diUXMkaMfDfMZMSq+PDueUovWrxtJKoFT7FF97vLBVFeMJ42PBUNQVWlicnLPkHYXaoAuT844QdjXltnNT6K0LZz1WjuGdw7lO8ytC8D3bl3Nbra+GbWNC6HEFdP0mX2j+x812XRHF58EJDOU6CvMmK0DvtKLflWFw9WXli7oP/w3wHwMz+X4LuAyhD+V+ROxE/ckSoJDUW1U1uJW0SnM1VmIHNrge/JPqhKm4C1OTcHA8IQjaMWBIBaY7TlMSAdV4UpzcxNloRtpV2gTPe1en/Obv/GMATq82LJdxsk5nr9NK1+nz9XnkaQRWqwWHwk2gF1f84OG7fO+9uP/PfvtbvPMnfg6Ar/z0tyilg1t2OGJk4wRfffyYum0I0lUqwTOXprSWDCnmpNUKLd122/WSlZjfh/MJajIDgTO36ws2i+jmTEdwNInm+3g+ZVUJX2Vu+NpXXgfg4HDCrVuHnAki8UcfvMfZWYxDONegkIwFirqOE3lZW1Qez6XynkVTU7Zx8o6ThOI4xiu80VyKW7F5csn9exEd6fyGSjIxybgArVlJQVi2yUmm4iaNxnS+vrNtXz1alQY6dz04iiTf8de7Dl3OtjuZgG6yhzDoBSOTPfQcA6FHEfoBlHjXLVCxRaDrGKjdjkl+06T/JDj1i7bbhWYPmszIIvCi8br06BDRqfWA8+EzOAWf231QSv0F4GkI4R8Nf75h0xuXSDVoMLuQsuO97GUvf/TyRdvG/atKqT8PFMCcaDkcKqUSsRZeBx7etPOwwezX7t4JlQTqNlWLK2WlD7ovDzaJ6Ru/JsFgJKtgmtgoxUpqv5hOyESDTVrDpo7a8OjkiFJMZOcd1Tpqlh++9z6/+70/4MPHUdOSjjk8jJDbw+P7lPNoKVyc0jeV/fY3f4byKub4zx99wOXTM949FVLU7z5i9UwYpPHc/YUYwNt4R34kefrzBVVZYiRiXa/XZLKeZqMDtBeGJldjBci1Pr1kIW3ds6++gT4o0YIBmNy6i30shWPZmgdfj8HBP/Nn/1wfLbdNSXkVA51X58/43e/8AVdN15/B9gSx04M5qbgFBQkHU2nbFwJrKeC6OD+jXJ8xl56fh+mERorYdEhoxc3LM81yJX6Rpu/NEajJkzlKnnn4uGRTxrGP7yvmUlditMJKsK+2LYnAn4NvsaYcFPkMzHy2mikq760r4DrLwjmxFOKfXKyEkM12rYZutLSj9buBbHVoJXyadXBj34ZrFoEWi/bm/Z+v8Yh9Kwbjdo13h5mUzxBo/CJt4/4m8DflgL8C/EchhH9bKfW/Av8aMQPxV3iJBrPWWs6vovnq8aSCaTchp+vcE3zTR7KnM0Wei99WOpIQyEZSQdhcUmQxjuCCxnTgGaUYjePnsqn5/rvvAfBrv/VPKZvA0WuxGvHorTd58E2hCcsSfv/3fh+Ax+9V2GfxHLPCcF9SkLfv3cFuLE8/iMCmj23JpXAMhhT+ueM4ce585Q0yuZZxBtX5ZV+p2VQNaRonpbEp3YucGY2TiH9IplyWEV14fnXBqlzQygtymL3JrVk0009X7/OdH/wWAI/qZ7z5RowbFEGBuB/njx4BniO5H9OTE+ZTIXbJCpyY7EUxIth4/LOPn5CK/X04mpB4R4JkfMZjZuP4+WA6RcpFsHXTc1is65pSUKNgqVzVv+BGFyhhmk7uKYL8bhUoQXoarXv2aYC6bYc9V/p7FrwfmP0DBOAOOUms6uyyD5Hz8fniKWMMfdWUYofy/ZMm2Y2pUqXiJO2yAWY7kV+0/9D8j4uA2aIwlYYehXmz+/Fp5/ki+TJwCn8d+NtKqf8M+B1iZ+pPlABYCUgVhUYJTDatE5yW/K8q2UjX4sWqJJPqw3E2oVCKXCDAPtOshK68cile2qov6gWpsCg9Pjvn3adR6z4OBb/8L/55RkIxPpp6fvQHvxGPaeFHP4gByM3pFUY6zJ49esy86Losp+RJykhKpNdtQypw6g+/9x7f+bv/NwC/8q/8ORDq9fbiHL86w64ljekUhDhhPRWJkJ26RlEJNqBtFhSj+IDPlpfko4JMjpnmNW++JryM5yWPzuM1f/jeQ57IdT44POFeGifuV++8RjJO8LL4JnrKfBbDQvnU0bp4LrW1PV391x/cQ0lQx4UHZFmOFWttual6aG9TllRigbi2ZbWK598Ex7lYKlnumY1vMZXzP54dcfvBNwGYTO/g+oCi7/1bZ22v9KM2DD0iMU6LrRXQpS7Rqi9D7traAdIvwm+JU9VA0w5aPMVAYbdT2PGNd2jZr+MBlOk/q4HWvx53uMn319KyPo6rB1pf7Swi1+MQ2993DrENoP5hWApDCSH8OvDr8vld4E/9JMbdy1728ocvrwaiUSlSwbVrXeOIWv/UPSMRXy4xqWDPYZQfkUkBFami8hV04CGtaaRYqqxqJuI+LPySczH/H581vPGVnwfgOz/8XWbpjCcXkcIsW6w5lnp+X9e4O0JB1jpqwbHfv38bI1bH1eKSZ1fnlNIs9mg8hTRq11vHU3JhOX7/n36PQgBGytSkK0uz7PzUQBDauTyd9gzIRsHyUlyGyzO6gv6NT3Bp0TddWT37MYV0iDo+KPBeWJ+bQy5t5FPAaewo3rMP63PmCZyMo3UxHSUoKV13XlNbaQDjcqbCczAbT/GS7ajKmmADVxeCcDQpQd4kV224WncW3YqlNLutbcNGytNHakI2ahhLjUTpT7EqMmWHlJ50IFi3pWUfxAd6X17+5gcxARKzo9FVT9Wmt+m8rgmtoDCzLCU1XS/JrWYfphe9c4NiaSFj6dODetunkoRhvH2rzfU1DohrFkG3HTfHAZ53C3jB5xejO19WXolFIUk0U2nwWrd+W2WW5yAVj56MbByDjqNiQivEq0+Xz7BKcdTES2kvLmjEVxwXBXUZX1ClauaTOHGenS1YXMbfZ+OM3/gH/w9TQffNx56f/up9ALSvUQJbHc/SnuRksa54dhk5By4vV5wvluhaMACjKQcH0RS//fYdzgUm/OE//A08d4cAACAASURBVGdkE1ngRp7XDw/RpSxyWU4qXHq1q8hyKU5qPVZSbSNjsPJKOq+4XNRsusCpzphMhPi1bXjnbkw9kiZcyTHG4wkTSeMGZ9BqTUpcVLzzNJu4YCXWkAkfRD4vSGUhuyivyIUUxWQTwsYyquJ9Xq9rzi6im/XRx4+5lFQnqWIpcYRNsyGVQKF2DmsVOukWxRFKSUGbUrQhXkvuR6gB+UmQSRTdA9UH54boxkDsAAad3y6EsjohEVi5NpokSUjkeZpEk3Y8CYN8flU3WEkJW+to7KbnQFBDMx91bYI/HwwEBTvYiptdge779jM7nz8tXvAiyPRngU7vC6L2spe97MgrYSl4Z6nWMaXWuhYnGiBNZrgmrlu1q7E+bvN48UEfmPzhD3/E0fEd/GEE7GDpm52mecHxcfz9IMvITdRGwc/5Jz+MZnW7OqM2hloKql7/qW9yLMCkf/J7/5CSGPQslWUjHOn1JuHRR9FSqJcVaR34mW/E7MXP/dQ7zLQ0prGXPd/jR8/OGZVRAz944xbL1pOLhhslCS6I1q/WWKGyJ+QUecwKbJZLgmid8WSEdhotdRFH9+6QCy35bDyjGAtzUB64LWxTachw0vq9bjKCtn1TUm00SZfxyBUTsZpKampxxcbzHFXL9q7m/Uff5+P3pFHMeUsHOMqSlIlwI1S+2ZY7jxMKsRTGac78cMzxrYhru3Prm2QqpoGrRY0Sq+f/Y+9NYm1P8vyuT0T8xzPf+c2ZWVljVjXtbjfdtsVguWHByhtACAkZMFuQYGN2ZgkSEmJlCYGQF0iALNYWkmWp3bJo6IEeasjqysyqHN50xzP954hgEb///5ybQ9XLylbzQDekVJ537z3n/KeI+A3fwY5SvN8rtO3Jl2ltMMKQCsQy6V5EMYlQz6M4JpJoxOh013bEoTSDPkNkzHD+znlsrxoeGXTP3VAtnp1kulL6U6nALvwfCo2fw734RcXBV0kF4LN6CvsRQf+6qirWotK9Ei2QVxmvxaKABy8PbFe3FBIytjhaaaO1xtOIXF/jAPn7ptZslhWfVOEBPTs5YbYQtONszmS2Yz/ORKdhcfyAsWAGxn/+Yz54/oKPnobw9/f/2Tl//qd/CEAyiVl3fW/dkcpDaMuEB8JYPPn2nL/+136N73434BHyJOajH/4ZAD/6ow95+iK0KisLp7OwQB1M56R6xfmF3CirGKVhwRnlik5wCp3rWMgxZ5MZa0F6+rpjlE+JxhIaNx4jD7/TntFCiF8Z+FYYh9sGiYQxOuN6c81oGh7expeMF+Hhi+YjjFybWCfEkmO7smRzE87lxUfv8+zph2yWIiDjp2xEINfrllRanUkcQw8/Tw1Tabvmkyn5LOLoJNQ00nFKJIxJ6zyRuGWFRsCuf28k7zeS3UdxrxURkwnOIk0ztPzdfgsQp1Gqn+AenMP4XosR/CDMskM0aq1vOUNFJsG6Xl9BDcdza4KzSx8+87u/gFRgf/L3v1utVsPkX6/XwwLQViWd1HSsdJFeZdylD3fjbtyNW+O1iBScd7QC7LFxTlvK6qgshQBpGt+xWQoOvqkHQ86jxSnz+TGp7BRnpwsenoWd7uxwQS4y6E3Z4ORs59Mxf+ONXwXgne99g9//4x/wh38UdvcPPz4nEzyB15YHi1B9T6OEqagRPzp9xMNHIfR9/J1HHD6YoaUguX5xzvVVQEeeXy2JBdRzNFtgtKgjvbzEqAvicQBAbZsOXPjO2OR4JQYuruWqDilTU3d0e4o8qnO0YlqT63wga62KDZnI1d8/vkcregoJGzrZ2X0aMR+dMT8NUVSymOEkTbE6Yi3oRO8Vkeg5tJXj/FmIxj55/wXeeYwY/saZJxLJe6c1XtIH0KSCQx37MVNRd4rnCfG0w2RCVopLKiUSeONDvOAXiFPUUDSMQO8Kjd5DJmS3fDzDDF0DvxNbxaEG5aSYAebkg65Bj/ZUSqNN7xGp6XrdDWs/5SG5QwtqbdB9FPMFnYGf1z3YH1+UCuwXCuu6vhUFrNdrtqJV0TXVEAl0dYWV6EDh0L02Ba9eaHwtFgU0tCKH5snIRI9gW5YBwwxo78gF3WfLDiOHfjg94eH9JywkNE/TkkR0EavtBZH4DjSFpZOFpynWZCINRpzzvXe+zrfeeQuA6SinWIkAy801VqrEX3vz6yQSLtZVTSydkHgesyrO4Sq8p3j/GdEHofVZN47oOHzP0XzBTP5m4izuwQPGszBJdOnpBHZdVzVJKmGy92Ry/rRbKslnt3WNU+kQ2m9vVozElPbpywveffo+AH9r/jc5EZalylKiefh+5YLgSq/7ULSW1oXwkzxjIpM3MRPKS/HNWC7J5AF9+/6vsa0v2PjAbN1st6QykdLFhHUvc9YoZllIZSJrBvGb3I4xnaMUfYmAqg4P70F6PEx+vIYedKT10H3QCtIsJRI2p/IWNZCg1C3fhr4R51w3EKA8Aebc11Q8em9S+qFLYa3dWzg+ZSH3BSjCLwIV9e/tvydIs322DrDdbm9N/D4tqIoC25R09S4dcLIR6b3Jb/BEPaEMBmj3q/tD3aUPd+Nu3I1PjdciUvBOYYWE0zQlVR1Wt9pZnOyGVzcrYgll3374BrnwG9J8wmx+RNavxNbT9aYxW8XmRroaVUktWICq8zQ+SKMpExFnMalwJ+aTjLGEvAeThEx289XqOVZARVcXF8SCH9g2S1SsmRoJv1cN7TjsYI8f3mMjCsiTseL+PKQic6NR04RGNpLtakknO0Wc5iip2N9cvSAS7oTxiqlAkXVnQWmuXwayUevAFCEKaJYbmk3ojCzPX3L6RJjroxGjk1AcbeuWcr3ta7XkswWJfKeKYhJJuaplyeUHAdTVXi15IaSxjz7+E1ykSA9CFFAWW45nYq6TjWmFBJXFKfcfh0il3ZbQSbqBI65jSrkfeuKHzkSk1FBc1EoRyf331uNsr4wN0WQ07PTW2wGC/UUhe2cbQUYh8GhH37pPkl1qsU/q/TQ1er/weJvi/IsBR73mQ5+avHz5cogI9guFtqluRQN9oVB5i8YPaUC8B9iyQOd3r3vdOpON0IJNSeX/rzJei0VBeTWg5Zy11PLwdCqiFYDN6cMzDsQ56ThfkAnwJFEtWfMhjbQbG6ATMZNYaTpBGm6rFZVM6m2lB3PUoqgwsebxw4AC3LqKn/4sEDvHBwcgbkdPX7wkkst17+QeR2Jkcja+x7baUJahS6GMYSQT+U1zQDQT9mSWcCDhbqxidJSxEnfop1fnmFwk1DqFFYNdFxmeX4RUJGshkVZrfX3Ner0lknpHEaV8+FGYvKaoeCi2awfTU7zk+nXjaCTczEzCeBqTSPrQoVHSxut8xkZqOuWLS24+Cdfi6vzF4Az+xpuP+fFP3mf1Mjywo8WEP/zBn4drNh4PBq2niyPOJ6LnoDwTMZ6djacYa1BdWEhW5wVGh8XbXl9hZtJ9SM3Akmy7bgjB88kEbcyglByZaJisTdMMoXjXdcPP4zjeOTwpLxnKLpQ35rMchf1J/XmmtF809heL/n3WBs2Hl+dhwX7v3R9RCNO2ayqcmBLvT3yNJ+11JAkTvtmb/FoszYOTVpj0STrCCMhMxxkm6aXz+zrPLx536cPduBt349Z4LSIF8ORpD4F11LLSt21DKqHs8fiQQzFeTZUjkV5yYjRxPKGqiuGzUhNWxbaoaXp1ItcSjcMaOEti1tLJSHXMNJsQy/Y2cgmH84V8vx2os9968A6TWfj+bGTQUvTrypok0YPle5ZPWCxCVX+SLkhyqQqXFZHQg+en92kqePYTEVJtE5zUE4uyoFiFY1P1Fi/RjbMe3YVd4onOGR2ccCVaE7/7s58MnZU3zhY8ECPb2cEZhZxXanKM8CsUHp9kFDpEJ0mcE0tnIwUqUXP+6OVP+fAm4Deuiy0Xz0OI+/THnxBHDWoqXpAXGQ8PQtRimo75UYi6Oqv5wfc/CNcl8+RJuJYHozlJNOJwfibfr2iFOv+yKRgfix5FPkML/jjKx2RyX1zXURQ1VgrK40TRa6zuKzgnSUIk18grhes1Hz7FOGzb9lZB8fPSgk+PT2MIPk8nYR9LsKlr3vvZB/zkh4GK79c35NIZCYXBXQTQFwcbwPUWdukIk41IJCIw6WjY/XWSYYbXKblEB6MkZZwmw+tXHa/FouC1Zys03E1ncHJRDydTxqJgfO/wkCN5CG3XoqUnWWw3dNYNi4ejIJbPmo5OKHWonneN51wky/JYMxLJttl8xmw8IZeHR7WW43GY1NODAyZHIt2exyihV2Mbtlehcm5rS4yj2oYJHnuPNj31uWMjqlK6q0nn4ecqTvAmZnJPvufqORc3IUxXzhCr3nh2zFyMcGPvcetw/HYDtfeITCVnecT9s1AveOs3f4WH4lSt8nTQNjBa0fQtOaXApAP3AK0HElZ7ec3H/0cAb7188YJ1GX7+wYcXg3flb/6r/zrnT3/AUgRsbsqCpx8GhOgszsmEr9ImnslxeB0pjZEawLauKYoKKyIzi/GUYxFzWcwTzi9Cveeqeja0Z6fHByRvfj0cfzanVvHQfajKjlaIZ8YYEvm5wwX/RxAhE1kg4/hWmqG1HjoOcRwPk9pae6s9uP//Ty8WnyvH5nuadvh9pBSHcj9JNTcvwz23gOqRl+kIIxM/zkYYeU51nN6a/FGSMpLzHCcpI6HBj9J04HREkSGW8+p/9irj9VgUACMCqWPSwXJ8li0YjUUYpdhQy6ScL2bUUoA5Oj2lWheDw7ctLF4KkpttQSl/57wiq8QiXY1IRj370qNcDQJtzfKEyTwo/4wWD7Eiw+5zhxekoV06Ei0EosizXF1zMhVVpariRshSeWaHNlxZXrDJww7eGU88jjh8Oyw4RlWMfxxuxfN3n7IVDYh//uFPOZyHheNr999gJs7cn6yvaaot9iLs/G/eO+DJo4AOPBov6GSC+GI7FKo6nRCLpHocTdE+o+l6gtVO4Wn1/IoP35cCpknIpA4xsS1pHr7vT370Oyymc3KpSSxmo8EdvKsdtZOJnB/x8pOweE5HGadH4jsxjdB4tutwngej+SCRf/nyHCP2erEzgw7C2ABVjy6NsZ1D6tFUOiMVbInRatBiDBO6d3FSA5mp3GwwxpDn/T3ckaX2sQFN03xmsn8eCvELowmtBtDCKB/x7a9/i3sHIYq6Wd3QSbG8Uxoj9QGdpLtaQZKSJnsTP02GHT+N40G3I4qiW5Dt/mh2yhDw+U7Ynz/uagp3427cjVvjtYgU8GqQb/etwkj4nPqIo4U4R2nNRKr6UZbhyrAGVtuaLMsxbtd62tjQ6ll2Ja3shlmWsxC/x8436JGEgdZgshyVyuq8yPBzqcTPM7qZULqjGtP3EJNowOonM0Vy8BCbhd1lU5XURdidxvEU04XvjNMDUgEFdXFHbUrGx2GnepB9nbEYuxx2I376fsjjj+495ul52Gmvf/aMJArfcV5comLNQ9kds7emHD9+I3y2VnTCQ8iz+Y56bMCIdPp8fMRyczNIxi8vzrl5GqKbn/zeD7ESkfzsw/c5WoRr/p3vfhcreXN2cUlV18Smz70DkQ3g6OyE1U2IAEbjEb/1JKQyP/jBH7AQybebm2fcu3+P0UiQiyRU0pJezMeUgsKMYo2WjsVyq1iX4VpodYWKIJHPGz18jJOIousY2ntRkmJ68JeOhh09YtcihBAdtJLKeD6rudj/zX4dYb+z4Jwbuhe33guoqEdXelAx44NQR9GTIwqJiK63W8Z9HWAvGhil6RANxEL17tMArfXPiQh2qg/7GpWvOl6PRQGo6KG9jpEIpM4OpyyOQiifzOcg+VSsc/IohMil3eAbP8BhtVLEYld0cnI4QIvjSFOXoQWUZyNcKlBWnzCen6F6XcipJurdouY5Vh58ZW2fwpMtDHosD5SN8PGETmoci05htgLHXTW4pSACjSKNZLFpGkgNPu+LYDH5vVATMN9r2ZgQJj95d4ltw6Ly0bLGSNFzMV6QKMVEevPTaEHZt6SO52TCZjRxSissP52mOCl0ttUKqjXVNnzPez/4E24uBV0YW5pVuDZdueGjdYA233OPyGfhXkzzMfdOHpBkYfFbLA742Xvvy2fbQYre2U+YCf7gG2/dI4vD5z64f0TTliSJoB0ThZbeeldBHvf1Ic1yGY6xLtdkkkMviyWT4zl1Eeo4yYMjnPhrTHRCrPoJ6gY7N7QfQnmTxFjvKAVCX9t2+J3eQ0TG2gyF6r6A6faQk/1CcFtE9VPFSWmbKufwKIykKeM442sPHsi5lUP6EuoAu7TglmAMrzb59+f/zo/C8qrjLn24G3fjbtwar0WkoBTMDsOuMX18zFkWCnDHkxFxIistGa6W9kwUIcVyFDkq8cOSODZTTmehPXZ9dTPs7pGygzSX8hW5gJ2y6THJ9AhELclMI0xP463AR/K5aTxIlum4QskOYLxivS2wQlF2hSdxgsnPFJkg/SbjxzRiSZSlCp9E2I0g14oNStB9PjUcHQcex2+MDjA/DlX91U8+oBIgVl1aEh8N3IWXz89pf/jjcJzLA3IBQk2iE3LpSmhthvZmtb6hfX7FH3z/9wD45OY5rYTvq8sVp+LKdXZ/QiHdk6K84sOPfgbAG298jfnpCYkJ1+BH/9f/yXe+9SvhXGzEfQmRN+UKxLMzT1PcQDTyYTeuRfmprVACjDJRPOxuBycnjPLwj5urG67FhLZUjtX5Sx5/LbhyqU3NSO6T1Q21tKtNFO30FIzCinxb2XboVg8S8X2FHnqQk6QivvlMZPBFxcXP84hUSg1q0tYE8xhle2BSRJyGSHGUxq+eCvQXZ//lXmTQdS2dPJtd1w4Ar/7/rzK+qm3cAvjvge/JUf+HwLvA/wK8CfwU+Le999c/73Oy0Yi/8lf+OgBtq8m73izWUK7Dg+jaAu+F8egvqARBWNeOUTohEYSiqxrKWJB74wTTi4fomHwUFotOtYMHQ3R2zDaJsEJ2ytOcxEpLa1UTi2aBNxYrQoQuiQYEWoQineVD+mJOc6yIsbRlIWqT0Bk15L3aRFQ3G8qr0JuPqppEJqzaauajMJE/ef/HQ8X9W/ePBwm6rnFEJmIyDQvp+HRBtwkh+/KThu5EcufxAYmE7NG4xUtfv24q/uyP/hkvngf9R6syykImzLIjO5Hz1BBLirIYzxmJTkJT3XB1/uFgL/dr77xD2Xc8PFhB4Z08eUxZhONv0FyvwvkqLEfTCbm0LhOboLzgTKxBSSpU2ZZUkKOjLuX8XEhb3nA2nqMvwvmsmo8wZ2HxsvOcsaSfhjG9DLpzwWUMQGmP1snAptTRHjbByx8DSutb+IP9OsItI1ulBpwD8CnilKQVRCine/lJjGZoq39qeeHnpQL9d1rb0ckz0+29dq7bLQSuw4n+w+dbMn3++Krpw38L/GPv/beBXyUYzf7nwD8Rg9l/Iv++G3fjbvx/ZPzSkYJSagb8K8C/DyB2841S6m8Df1P+7B8SpN//3s89iHxM9mZQLoq3G6JaeAyuo7Ch0OXqLf1ibKt2IO2MximpTjGCTRhlY2LxZKgyjwQA6AzQYQdRjgETrhoYZTErkR2zylAILbqejJl2O+Wftqe6eocxUmhU4FwDIqJa227n4pOPh5BZa4WTwp7tapLSknYCmLJQiTbC8vKG1UvRFtAZb5w+kOtt6cShKYoTyGK0nOfpbMH0OPS/y4MR9Sic28nJA3IpZrZtSSOkqYsXH3Pv0QHLMnRprlaW6Uik0vIDlmW/6zdDKO0cRFIMzEc5Fy8uaQ4FfHUv4eOLAMSJo4RUQvafffAepfA1js/OUNItuTp/Bm2LGUsonhnowrHFtiMSoEGjSnIhgR3OTvDCQ/nR99/j6sU5E1GlOowiPn43IAUnj065ENOYR4+eUFlBqs4PGI1DWhQTEbZOASm17S4d2O8kWLuLFIxBczuY53O6FMErYn97D//TBPOWqI9IwpfJ3/jPTQVCNCCpgG3pug5r9yICt0sNegyGHM3u6/2X3/e/SvrwNeAc+B+VUr8K/AHBlv7Me/8sHJB/ppQ6/UUfpE3E4SIoEPvjhlZUgn1n6abiWrw8J3ZSYTYjOqlKV6tr6rYEuVhkhqaVi5Id08pkbbdbjLTnXFWLMgPka0ejO5BKuk0i/Cg8iPn9R6iRWJgRk/XWYWlOJa0mazSNiuhxxukoRfULge+wkge3rkFJV0Q7SGJNLbFksb2hrqT6EaccngqiTecoaal625Jmoj6cKLZdhZYUKB1PSKUzoB8egJjVGjOhFSBUV1ZsL8MicPH0ioubS642Yt6LI5NuStXU3FyLmU7bsW1C5hepiAeLsEA9vn/Kuip52is4f3/FSERums2aE9F2iExKHIdjub65QLqLPH58n9g5Slkku6JjPhPwEC2d/HySLShEGVrNppyKQe6zj8653hQ42SWeffQzIgGj6WeeySS8Xn3yAYhcfz4dhdWXUNMwztPR63/aodUXx/FgchMn6dDx2E8V9v8Nt8N/u7+Q7LMkMSj0IPXm9tIPa7uhDmD3agLW7ib+kBZ8yvS2H8Pk92qXKfhdheLLmMF8lfQhAn4d+Afe+18DtnyJVEHtGcxeX998hcO4G3fjbvxFjq8SKXwMfOy9/z359z8iLAovlFL3JUq4D7z8vDfvG8x+9513vBJgjjOghbiETZjmgQY8WUypr8NOp+oG3YUdzDU3bJbXWCn8bfOE8YFUz42iFZCTa2IiwRx0zlIuQ4/7ZrMl9ppEdp1xlhML3fn5h3+AkWJeNJuChMXZwTHRIoTrOs6ZTw8pp+F3XdNgeq041aIESuttMcB3TamoL7ZsnPTz8xGJvH98f0HSivLP1qEFlLW9uUZLiK8STdsppo9Dld9mMUrIQn6WEE/6lKHBSKFvs7xkeXkl19VQlprVtQB22ga7FAwFBt+TyMoNyxsxuZlkrIVH8hP7Afk8xwqYa32zQs2kt69guxZo82RBLh4So/GIXIqeN5fPOZjNmAhFepIe0JQ9Ca5mhqhtrUucpEjKTKhEneubv/IOz6dznn30IRBMfzKhy29erskFzj6/d8pMistJGlFL+rS9LsmiDD8Kx5ymGVowE1rvLOYU0AjTSiv9Gdm0AdjE7j1xHNFj7p3rGHhXvqNrPY08p6EwuYsU+rTAum6IgBiOYjduRQR+/3d7BdE+Ovk5hK6fN76KwexzpdRHSqlvee/fBX4b+IH893eA/5JXNJj12tMlvdO0AQmxvFZ4L0AiPaHpwqLgGovfhrQgaTpGXrOR6neN40T0E9XI4IRbHjFBC1jFFI7chXy4y46xPqK7DpXxdb0mWoWHejHJWX4cKvTNdDloMHQXG4pR4Ae48YLJfU100ANxNFoMSBwxba8xmEQ4Wfi8UUSjOYtjmcjFDZF4ZmrjqHs57nXbR7y4NKETgFU+mzLLIqLTMOF8Gg1OybHeeS563eKk/7FpllyVQY25qTsOsxHPqvDhV8st6yL8bnE4HeTgpklKJK3aroOXQgJLuwhTGDppER/OTzgRh6ppHlGIyIvtGrpKugJAJHoYX3vjOyhf04j5rM5TjLAhvW0HNmNTrqleisZkHg+u0/nBlEl1jLsSpezSD7qE0zwmskKuu9zStoFEtvrh8wEsdXW94dGjN5i+FRZ2MxrvwnrniCX8j+IIK/WJpm1udRj8Xh0gyzLk0HAerPA42rYdagLhGnZ00np2fpeyeMdw/xTqVh1gmPiSCnzacSq8/mIzmf1U5lXHV8Up/MfA/6SUSoD3gf+AsGT9r0qpvwt8CPxbX/E77sbduBt/ieMrLQre+/8b+I3P+dVvf5nPUV6B7aWygL636lp0X3RptoOFW7vZoIXq3FjPZlNipbiYpRNkAyI/ORl2yma7xArkOMo1TSUyaaMcZSO6LOy6RdWxKkOYOa1AN7IDNxlTEYett1ua5U8BSE6PMVNLbMJOyXiCE5ZnnUX4Hr7qDEbgt3UGKEWShjSHSYTWErLbCisSbnrWgMB0p6cPUL1wrUoweYSeuuGaxRLKN6uCcSHdj7bjWoqGqYkH7sEnyxdcLq9QUvmbHx0RTYRBOs36u4CrW9pCdm0H08NwjmYcxE2jNqRGB+mMe1LQe/zkDX78UYgIPrl4OXht5MkULwXkzVXB0cmCVo75/Pp62FEX04iRFErTOB08HtuipLgR9mbaMT465JvfCR2rP//9jsaF6KqOIdmE67QtS8wmRHqu81gphuZRRBwXfT0WrdVO2s1aNmuJ1EzUB63EXRuiid4eLooHWnRdlniBWSdpMvAoyrLccSq8x7OjYisUO+Txvrt0UI3uX39RKvBFsm+flob7fyNS+Asf3vuBA++6CtuE6rmrr3BtyIkTX1BJiEmSoJMUL2HqXOW4vhExm9N6CVENOOHT67al9x29vFkTFS1p3N/siJk84M2qhoUgAqdzuijcxc3qJcqFz01vOq6fPieZi5r0kxO0tN6mD+/TyOLltB4EM7QKzkcCIkRF8c4nUoGSXNeYCtvTha3D92mJcqRZixbucGQTKunGFJcXrKsweZpyg2rDwtO1Ddsr+Zt1QZQq7t0PC6FyMVbLgmfgSijNJjM8OQkTHN1inegF5pqi7QYzlQdnB2TSrmyqDZEKEzyLFWMhmp3NTxlLJX+9ueZm1TCdh2t7kMVsJWWzvuBF2Uu4GaZ9fr/doqQmVLkaezrj4ThQ0e26490//2MAri+fUioxpkk81TacS6xjnhx9IxzLG2+QP3qMki5JW7ZUEvJH8RjbA9EIKRCAogsmsnLPtHcYeZ5c29BUkkuoHbrRGHNbvdkqvO39K2P0ntBLz/34+b6S6jNgqk+/55ddCPbHa7coOOeGSEH5LbYKOaEvbiguw6pv6mQXGWhPk8fUhTwIqsUItHa1uaKTCdpgBkNS1TXUov2oEjBRhi/De5I2ZvU85MSj+YR0HHaXrlix+BCTgAAAIABJREFUFfbh5P4c6TTSXV/hC0tZyO7SrUllR920W7w8eGo8IZKdyliHJkYJCNu6sGiEP4wGd2mfKrwQumxVk/TKQfUN3aahXYZj9pWnLgQ72TUg5LKmqPF1OC5vI9JYdCVP7rNq14Na1aTT1KI7kI4yjEDAYxXjZOGZRTFGvCGqSHG9Xg7q653dUraiO7Hc7W6LyYhU6ijb1SUvBdbt84gsTbn4KBzb2eGcewdh8dmuPUkv1opn3YQF4l6+GLQhGpXgri9ZSh3q6BsPeTMTtaI/KtkKUtTqCMSr4+jkIU5qCsu6Zn3+ErUM369NzEwYrE3bDbDnutxipdBoxyk6S0nkfkSYIaDFeTr5R901A7nJGLPDrGixo5MikYnY4SFcaIWCeHp8wQT/eQvB5w0vErUQnLlfddwRou7G3bgbt8ZrEynso8CctOHizqEq6RhYx1Qw7ZtNRSf5Ka4mjT2HT0LrqS1alpdhp9AffsKDd94Jf6c9jbQAm6rGSCg6XczZXhcg+frWlowehl1jHMU0YllvGk8soCbbMPgdts0av1mRmxCmu5srLq+E0nuxYCJ6ibVZ0gklOB1NiA6mw+qt8xjiPiJoMIIC9KbGiAFk6jSu7oFcNc1qSbuVNty6pip7/0kGJ6nOq8FXRbUljbRKs/EIXa+4XkmLMpkPqZUvOw5HvWW9YyKgqOVNyWrT8xg6osgzEieo1naoHtilJqwk93emHgxavbfM5PqVaYxtGg6ycM5T3eEa0YBIYxIlEYW9QSfSSWj0EMFkscGXBUbSETuZkT8OHaevbb/Hzfs/AeC95XNSAZW9ePoCb8L56mdPScYTTs4CGGtxsGAt16Ktt2iJIrUypGmILhJ3SKpMuPlyba3kEjqOoSfR+ZhWUhFjoiEC8E6hlcGrvg1pB4UxE5khutB6l3J8kcnMp4cL1rfy+stFBZ83XptF4ZYsthSXtqXFdwJHbrvBESlLI7TAkpVXNNZTSEVolOWMZYIUT5foN8MNjqN0YM+NM/C2lyyDzORDOnF4coqW3KBZLrHlTi4+lRbSZlvTSi8/dimn83u0RgQ6q5Z0HX43rmPqq/CAVkYTSWFv3daM335wq6bQW5DpzmKFTenSiKhPX5Qaety6a6F1lMVajqccnLA0KVnvitUUxL4Xrl0zNb0FWUFjm8Fq7rJs6aQ+EGuwdZ++dZy/eCbH7PCyKC8O52y3BUsxyF3MD9CT8Fk//MFP6ZSQ1fQ1h6KHscgUN6Xcs3TEOE8HCHrRlKQi7JIlEbHk3RERm/5e+mZApMZK44wn6sVIRyUj0Xh861d+gx+IPd6Hq3MiuefbohhMZJN8hPKKTofUtKkKti4cW1mtiOQ6jSZT4lnAgtysaiJ1vhM5yZIBdj07ORxSq0glQ30giqJbgrCwaxEaY4aaBKiBxBTHPdb29ghlyv3J77/i1P/icZc+3I27cTdujdcmUuiHUmoIpdLjBzRiX97qJd4LaWYcYSKphGuHt2UAPQFuXTOWKv21q7CptDHRQ7jZlppOWpK0jsTE5CK7Vm7XeJETU5sWLxLpahRTtCEs9leb4crVVQvWgjhM1e2GqJOC2LrCuX5niFj3YJ004mp7CQJGwuwESrXSQ6ErW8woW8HeJxm1UH/zOOHw+JhMiE9Kv2RZisT8dIwWGvLxJAKJOgoz4kpC9Bd1xZW1dL0hzqahFELZNDGkkpktr8/RuSD9xiOqNHxHqUuu1zfUW2kpXjSgzuX63aCkmOaMZd2GY7nMCw6n0m3IQ2QUSeEvH41oBz2MGG9FrPemIunl+OoGLSF6Yzsy5yjE/UvFCXosPpu+ZvIopH+Pqgc8/SBIzPttQSTRhNYdTbuhLuTzvCOSXT+LYh48CKCmqmxoluGeb1ZruqJhLB2T6dkZUR6uX910ZIMnrhk0HJIkuWVS0zRNKDbK2HlW7pyjnN9FA17tUoEvExXsp+KfVqF+lfHaLQr7o1MRiIdCYnLMKDyU2+4TnO4vXMfRvXtcvAwPZV2UHEiYmzrD8jzc1PT4DBNLe87UNEKUigmV4aoMC4Ev1iiZYKmLsRLWJ5Mxn1wF6fF7kWUj+X0W5/jEDlXluHWkps/JQUn4mHjLWM7L1g1d27EWLcnGe6JpeM/zmytOH4Vc9/7RhFyIV1vNgMhzruNq9RwvrMkYmAucOs6SAZ1nu5ZaFqJNUdGIEExVdURdNEiYqahjKvl9rg2uCtciixIOzwKfrQQUIodXjllXmo9fBEShSRR9x6NrS07PRORlPhtUitM4x0u4XHclR2qEksev89HQvZmkYKRj0c1Bi3hKSjqkCyqKoWtR8gyY9QVx34fOZxy8GepLTwy8fP8T+f4C+vSJijyfYUU+3nhL1Au7jI+5+iSkZUkaD9qTSRoTZyPyg/A8zu/fIxd/CxenePEaQe2mVLfnauWcI4qiAbeAdYMGgtIRkcC5a9ei9C6t+Hnj8yb8F6lP72s8/KJxlz7cjbtxN26N1y5S2O/FesD21dcsw8kqPL5/n1pQa8pV1HXDSNyj/P1oAPXoUg/Kypk2e9XinPFEQEltTbVZ4sSmfmocmzbsrqU3jA5DdHG9vmbcO0/lelBWbuqWCDWIqo7TSUBoAlVZDYu9t45MOhZeKTo015tQ8VZ5zPPz8Pqy3rCg1xPwjKXiPktnWClCOddSlgXL69DliFpDrGUX79oBR1+UFUXR06ANXkLXQ59RtiWdpGZqEbMVgliUTCkkItCTlFYAS6Mowdrw/R8/v+L9n/6MeBz+fXI6YSxkrbODN0lH/f3TXIsTV1W1+DR8fxvVbON7ZJNd5NP70tRdzXQifBc1pu0l03QMonylvcd3TdCxAJq6wgjOxM88zWEAYh28+YS/+uv/EgB/8sPfpScVT9uOrKuZHYeCbNw6xgJea6otTd3T3SN8T3TKc6YnZyweBPp2Oj/AihCvN8mAOVEqGtCRCvACW/S2w3u3I8uhh5TBRAoz4A8YJNx6sBbsZN4+13Tm01yHHvxn3aDn4a37zPu+aLw2i0JfSw0nvQt5dA8FjTRG2obeZ8Q+5JrNtqVYLVEC0xjPZnhh3BkVY0TyS+kEnQrAqdH0nJPtdkPSQSvYo/JmiZdOxOjgjET0DuPaDa0qT03TCXopVrhuT4oriQcSS+cKEmk7xT6i2vRol4gkSRhLjtzZhpM0TND5wQFHkrfmyYimd2p2DlsJQKaoOP/4is2VKC0fH3MjC4RS9ZCrbpcFVRW+/+T0MSqSSv6m4OpmxYeXIfzfFEum0uWY5hkn85AyTCYjiiYAubZlwboM3/fx8/eom5K3vx7k2yejEY/uBTOacWIotMjSr5aspaXbtZpUnLO8gaK4opCUIdZ+WFTKVrGtxQxoMicVh7AkMsi8panW6D1zntbGeOkemKxGbUKtIc09b/1WQDHmj+Z8/KOgY1lfXpImEaYTVyhjhpZoq4Kxb/is0SDLf/bG20yPT5ASD41StDLhVNshzSdiney6Svq2hRzshfd+b5LuTXRrLa5//vlsKrCfBvTCKr61A7PSWzssKre+70uMu/ThbtyNu3FrvBaRwr5mLdzu5WZikKk9bFahsObzFCXSar5e47XGlSHM/aR6wVy8IEeTEZ2syJ31Q4hdtUsQfH5bl0RW08tDt9GE3j+k8zHNttf9D9o5AFg/+ElobXGqAylIbqoGI1Jx0TjFCaGqbhyuN5CJI5TyLBaisDQxFNIxqZKc+SyEtba1lE3YTY8mI7xECrlzjKxiIT6XyndsNsIdsBsisbTT5GRRzyO5HNShnA/FujwWfYdJxmgUjn+ex4xEA6HqagoJpStXcrEJ12gUaX7jX/4tJgchovFtTSyRUxuNuRHI+fVmPaQIWTQj7eHPecYozfFidYb2TBYhIji7d0oue1W7LtGiQTGZTai3ElkkHV1XDDiLhYoo1qGg3EU1iUSHdVzgJbw4fuMJidi0Xbz7E7rVikp2+rXpht216Dwjuf7Ts4ccPQjgs2R+SNW1OAGWqc4OhV4T60GN2pkUJcVV66MhQuiLjkOo79WQPrRtS9o/W8YMHQoTpTvNhi5EA14igv3oFD4/Ivj/FSFq34UnF5ZdhmYkyr617ehKcTx2FQpPeiSIxpcvBwmyKqmwMkHieo2eSd9omtGuws2a5Bm6aDBCiPdmgpKF5OzRI55+FLQHHY62fwi6ili8L6NE0yo78O5HKXQuhMzKRoMrkx7pwWSk6xygUVIvUJlhFIVzm8+PaHuJcVtjdM8YLfoCP760zCdjmjbc8KIsOL3/JLzetpSbEEqPxzHjmcS7cYvvSWREKO+ZCCJxNFmg4/CQrW/WlHUIvxtqbgTFeHh4wtcfheM6fvtXsQpurPhk1ltm4lPZ1TU36/BZ19sWK9yFeeo4FB7C5OiIPE2ZHYZFpfaWWBicV6uavJcuXK2JRYG6W15jBYiVJKB1S6z35NeFC+PWK2wsbEp7PjAR27TFH4WF95t/9W1+/x//Li+l3bhKXU/SJU+nHEtNYn50n1Zk8rarZ3TNDcb2UoEehywQWYyX9rJPUubyLEbRZEA0JkmCtXZ4tq3dhfwoNXQifNfhBdHbdvbWbvlFnYVPpxVfddylD3fjbtyNW+O1iRT2V70+5IqiiFjSh3GU4kQEtLY1bdqzJDOaWce2Drvz0fSIqBJl5ZsbtoI/cGVBIuG6jTS19IujtsNuNxipJDdaEUn+YPKIopX3N2tGfTUpShFnNhrvpaDZh2ceL2G6j9QAX0Zp+upmaxu8VYNlvTY5sRTUongKRoqLecRmG3btm49fsF2HY75Zlay2FZcC3ulcw1hMZ95++9s8fhB4ALaqwNf9BQ7UX2DrW+azjIMjKfzpluc3oYh4vr0Yqt6xSVhMw653OD0h78VZy5aLy0tKMYOpY884Dec5SmboTUgflhfvY+S+HuUjjk8F8nwyI44irIjS3qzWrJ+JWlPZsOgjGM3gqziJpxiJ4NrVhlGu8dIxKroNMeGz88RQPRfMSRyjjkIBVs8KLgWnYOJDvvEvfof2Bz8FYP38OY0UkWfHMaU8Sx/8+PtoKUaqtiY3DiN4FJWawccjMkeMZiFlzQ+PGIugrq38EB1CiHw7iTSc8rhebNgrrPzcmB3k3fnPRgOvkgZ8kZ3dq47XZlHYHzul3N2ksnGOpzcB7Yb83E3SAIopQjjf+RLpFpLoBC1twPJqRSZAnK7siCU/d6stsfVYaXNobehZRM+efkhuBIU4cgMuvUMTSVehKxq8dWgv/7Z2J4uOHoxFnGNAXXql6Jwlk9yzq6MhNXHLJZ08oFVTDLb0Shs2sti8+7PnfPDsJRdSRzh5MGfehAfx6OEZh7JAjMYZ26uwQB4eHdEIPbqlpnWWRhaMTbllLXWAKDYcTMNn4SHPwsJx/+QkyJQD126J124wGnnw1mOmhHN5ePKYXBy6TmYJq2Ugpy3mY47mYeKqNqMuPE+vgsbis6tnIHyB2XhOIw5bqUkGTsbVzQV57/fpHN2qpZN0IpvnaB+u0/a6pZO0ajQaDejOuliRSH3BHmfMH7/Btw9EA+OH7/HuHwc9BnO54XIVFoV20zGTOkSWJFRGsxBZ+2oMp49CykZ+ACKY46KMQsh6sQfXG7Z0Hud2XTalGHgUSpu9yWsGwZUkyW7Jue2PL/Ky3N9UnXMD92IATb3CeD0WBb8rNe7nTW3bDCQgRYuSRaH1lm7wYPCYqCXW0iIszvEryfVWBXYdfm5Sg1uHz8q9p+sLjV3LtrYDiSVLFK0U92q/JZbeehbP8SpEGs4amr7ukV3jrRkmvG6hNysNilLyEOCHFlKWJzjTgdjA+abASs/dNhVW3K9sUQ457MVqy59dhol7VRmWJeg05OQ3yy0HouGwXd/QzqQAWLU4gRnfFAo17iMDA42jlaLZ5no75OQns2OcMAFNZLh/LxCCkiQZHJxnCk7bimgcJu/pk8fMRAo/NjvU36Mnh8NiFxnLSDQaI7fgk49fklUh0jlwObkwKDurqXo4t1E4QVqqNKFpxPlLRfiuZSLn07oO3/twOIuWBd8W53iBYtdjNeAqbKu4aFrcYTi3N3/1Cd+4F9CJf/q7f0ChhHiGI0lkZ45h3bUDM9SODYUQ3KbZGCWFYt85vGgw1L5F+Uo+S4GPwwcB7LXbvf4UIVA2NWu7Wzt90GcQDMieRPwt1+yfg2581XFXU7gbd+Nu3BqvR6TADr8RVr9ewqqiER0/7w1J3170avh7ZRvKm+dUy5CTmuUztk+FKLOJsGILr9OUq6eBHDM91fh4t7LGHlQfOfgGJbJr+egUxMUJkgERmKaOqNdmUAZtFQjdN4uMGBJC1VYDxRq8GIaGqCEeAWrnBZh0vS19ixH0i3eWSghNLy+uuVmH91+3KY2JqCR8n4yOuOpCvv/ysuX+JBzbIk8gllpJ57FFuH43y4Krpy+4WoY6QpQasnEI/7fFlkjC7JOTewMmP5uMBvkw5xqOj3JSMV2ZpQPYkG1X0XfLJrMDdBxSCetrfJ+iqTlzb9gK92O9fcalIDqL2nEg1f+m3tKdh3MZpSNORiGUnxsFyuKWYq4TG5SkFmk8ZiWq1co7IjmYqNFUV+H6TfI122pDLZJ6UbZgPg3RTXw6IxVfzvFUo6SOFU1GTA+PObr3FgAHhw8GDYWu6VjLezrXgnAqttWSsVDKp5ODwPXouRDG4M0ulejHPngPdqQpCB2L/TTgVdqQv0xX4qsazP6nwH9EmMV/SlBzvg/8z8Ah8IfAvyeWcq80gp6+vPYdnYTVymm8WIB5a/pUnarY0KyWsJQ24E2LkQJW40oiJ23IZcPmvXDjzg6fUEuu2LoltutYXof8fHJ/yuFxCL+3KqUT7T3bdkTSAtOqhL7tCDgX04n5K6oZYK5ohxARabd2Vx9RHh+pQR4O7fB1Onxej5nIDtTgZv2wg6dNkKP7aXFNQ0onD0/WKmKBJisz5vsf/TSc/3bNYxF5OTo4xQs5qtqWXG6WXKzDRDwZHaOkcJtmMblMvnXbsnoaWrJnoxWZFCBvtmtKHDMfQu7ZwRgjEPDrlxeDBFg9qrj/OKAeV9sLTBbuX+kbpmcjFkUvWvMIpcJCvjhKA+EJ6KoCL+S0RAFaPBMSg0s1rUzqRBms6Gu0nWE8knvebHdtZGdQjWherJdo3ZDIfNGzjjILR/3Wtx8j/sCUqw4r90JPcpo040o0LHz1Ca3oQ+AbtpIKVW6LlrQiGU9Q+n74k2iKMTmdtBstbkDORij0AJMOaRuAYmdc627Zwn22iLhfgPw8E9wvk0n80umDUuoh8J8Av+G9/x5B5/LfAf4r4L8Rg9lr4O/+st9xN+7G3fjLH181fYiAXCnVAiPgGfC3gH9Xfv8Pgf8C+Ae/6IP64opCD2G2934wz1A6wkmFv7Ut20Yw9fUSXZVE0q4zW0cnZq2pUcwEKbd6XkErXYEOGkG1dLkBazicBuy+SxWlaCD42OISaQ9GikTCva714EPRkdjjbUcrIaOzJdr1VeGdRPh4llGLopF3GRaD7v0o22JYylVkQERM46Yh34bd6MTN+JakIkunuajtoDYVe8v2POzoV92G54I8nE9GXKwC3v8bbzScSbU98R3H9+YkhyGMaazm+VLSqZfXZKMQNRXaM0t6s94JiRTJLBqVjZkdhOLmemmZH4ZH6eXHz1iLRP53f/1fGNSSZuMpbSLcE6PwZcfpG6HQd3T/kMcbUcouChLpytTLAi2mP81mQyZpXVEsuekqjoXQlKURjQjXFssSL8VpQ4s0ifC2HqjaaZLgt1u656Ez4gooZ/LMnJ0wexhSntXDE/7od/55+P4Pt3SRHoRYL62iE1PcLE8H2bhkdsB0Hp6l2cFDtLSam06TGD3I48UedF90ZD/kZ4gOjFb0qXQURbd5D58qKLo9vgODc5TeM679S9BT8N5/opT6rwmGLyXwvxNMZm+8930f5WPg4St+IiBVWL8LeXw/wVRMb8flaQY5Nr3d0p2v4EXIT7PGY7bCYEwcfiJ2cKdzGnmoLtYF6VwQhG9HuKuSWkJB67qhjaTyHC01DWccVmC5TRdjxEXJ+w6tS2wlDD6bD8SltoFOqvpm1KBML80FRsc7HwivcfQtUU87dFwssYTcsyjjmyKz1nnDP3//Ka10PK4vtpTiMFXfNKzFKbuYWE5FDu35+RWVoBN9U+NNi5XKfpvMES0ZisYP6LqNvmF6Gh7wy2LN43tvhvOvLHk+Ixf3qOl8wpUobadGMzsNrd/IxBTS/VAjsAINV53G+qLvQhJFGafHYvVWzdFW8Ch5jRc5ts1mAwKlVrMMnXSUtpfvj4gEURnZFicoRN+6HvJBbBtagYTqKKLYbGkqaWkCWroE3UhTi1tVPJ7wRLoS719csDy/RueykGeKaRSOOVYpoyyknPPDNzFSn/BJhDWy2CQT4ljvwZ5vT+TBCMr7YeGRnww/d87tpQFqD+14e/Lv0odXZ0buj6+SPhwAfxt4C3gAjIF/43P+9HOXqFsGszd3BrN34268LuOrpA//GvCB9/4cQCn1vwF/A1gopSKJFh4BTz/vzfsGs++8844fnHe8GwpV3u3CqjQBJ339trxCi1mquV6h1lu0VOnbqgNROfZFzVUVhEcbpuhecmudMj0QIFPa0pgl6qUoMNcMobzJFFe19LZ9QycmppGOsK2gLo3C6WQwsOmKYnhtvB/s19umxRkpWmHpuoY4CgUxZxxxLrr/ZUMkICnVgRdOhTKW1Ied6cFxw7ftGd8/D7tbtW3JxO4ojlqOJnL9ckMhgqBXZcWqDuHuJItIIkOxCtfz8IkbvCyfvHGfjZjGvLV4yKGkHK7TTKfCCdAxaZIOvIpoljJJw++ig4xU8ACdSrEigZdkZojA6uqKprwhE4Wq5U3H9Cjs+soZnEBOXBuUiAD0YYKRx3UazXG6xYo3aNQ5YkkHo2NL8yJcy83LgkoUslxTkQo6tqk6rIt6ISaoikHFiesJHWHXV/aKb34rqGBlpuOP332Xa+F7VDgmkv7ND494IjTyNptSCTo2Hk+JRQUsjgxur3vwaY7PPk5xKBoqhhDCWcEwfMmIYJ9r8arjqywKHwJ/TSk1IqQPvw38PvBPgX+T0IH4O7yCwSzswgmv/dDSwuvhoti2ptyGidvcvMCJjLhflaimphEp+GpdoCQVMFUN4mBduS3zLCwEm/MNXh7Q028eYWNNLa2nXBuUhHxohaS30EYkvehF19E0vbZCsNxI+omcgJLFy9FSFD14ahc6KuXAqGGRIFbUImuuYg+SskRRgva7RSkRINXpmeJ72RQyMdl9eoESmHE+jfFiQaeyGUqSauXhQOTalQ15dy9MkxmNEeLYIp3xRCbC4cEps9mp3ApNIS3QKM/RaU48FuRjlJJI9yVTNbloF65bhxKiV72pqKQ+YoynrRyb/n6WFbYLK1mqR2RyLImJMf390xWtLBCJSukqTRSF9EWnHiPXsrHV8FSrWBFJrzSNcpxMyKZtaYFESHCqsWhpD7tlhYrk3iaOy0QQmY9P+Top5++FPa5br4YU8ibp8FXYpI7nhySi7KxMjJPFarstsZ29ldv3smtOqaHda3TEoCei9GBNZ53/DDNyf/TPlrV2WAC+zEKwP37p9EEs6P8Roe34p/JZ/x3w94D/TCn1E+AI+B9+2e+4G3fjbvzlj69qMPv3gb//qR+/D/zmL/uZofDfV2IjvBTTqqJgK3x6bIUR78h2u4KyYyRV8iRK6ERDYLmpmQoUNqVmKyYtpalZrz8O7/f3yQ5P4CBUnBtl0KJQtHZrkP59ToZtey49KOlEWGqU1mgneYLxWNmpLRYtfgZRHmPaXtvAYX03REc6MgMJKU1GaCladq0HMcj1riEyvd9lzhzHW50Ajs6vsHKczBTjkxD+no0fD4rJ48WcVHa21eVzHC35LHQPIjUnsuE9h4sJ4yPBPxzl9PmPw6Glx25mMT7VlLI7qzYZ9ARMmrIRUI83btCgiBLLVI6/rTJG6X0O8r5Ls2YlXpLVumQ8laKtq1BirOJdO/T/GxW8PmJJNLPIDKrZtqpJR33UlOJFD2P58gI9+EWmRMYMykm26UgkTdPumpEoT5lmzs1LMcw5SXnyzbf4+tvfBuD3/unvDH6keT4nFWm7crVEmXDPOhxenqUsm2KiGaovImo9YJQcHt9HYSba6z5EQ9HRGE3bWLpuFwV8EYZhf/QkuC9jK/kaIRp7tJ/C657XwCA4oWyHkuq5bkoiqTybrmSzXuIbCZO3kKqQ003nGQpBp2WeQjD1qnaoNkyozdOCOO2Yfj1MENtY8lpal7WhiELrEzsAFUPNoEcnxoa6ramEBJPrCCUcelzT20LSqQ7dsw+JwFu6/pxtRisgqbqrydLdAhNJib4qFZlItsVxQhy3nIre3zefnKGE8VjPDWYRzu1occbJw0DaGR0d4uX7u/Yb1MtrvAjAJKRkcp3bZoXugVA+oRLV6whIJcSOrSIio5H31F6je4cop3BNj1wE06ssuy11Jw5NakIWj4fzWdYd2UkIuZNsydNPAiIxMzGp6FG0zpHLtXCuw+DpNiG/r22H6w1/dUNTikq2rYnkpiWjEVEsLMvGEnlNnyXGcTJ0IkxUcy3gr6l1pMJ16Lii1jGxGAk/+vojLp6HTeb64yuKpwJmiwxOEKHRKObkJCwwiR7hYwatjvD/XWeh95r1LqZfLZxzaL37m6ZtaOrPEpuUUsMCo5RC3Yr/v7zE+x334W7cjbtxa7w2kcIAt3B6AP9EWoP07LtmQ2/1XBUrsjrsEl25QTs7OAD71qIEVa3MDueQRGMG3SFtsf2uWXVUL7Ycfke+X+lBgiCP8wBpBlTS4UUFyTo76AREKsMSozM5g9oNNGAdJwO/QaOom343irFtBX3K0aSksrz7iEF12Do7qFnHo9kg6NmpmDSLQ8UG+NqDXwEBaTEdMRWxUT33fH5zAAAgAElEQVTLcX2HINaYXni2TojbEUagvapTZGIGU3QVVuDQ2xcl24twzdMoQ4skUjM3pIspsWgt5GkyYBtc7dESHnV4nIT8k2hOI/TsuqyJTQcSZh8eLLDCQK1VwpEI9GYmYXMTdv1utWV1Ln4MymHrFVoYpOM8YyrYgotnz7EiwjqKM/IoFFfT06NBJs6VBXZ5zmCbrQxG5KRd05EIEM3eXJCMw3U1dU1Nw00Xjufg/pT54dsAvPsH71E+D74jXeRYPAg3ZprPSONwXlXZof1qAC+hwEjKq4xB9bob3qHk+fH4Pds5cbEWDQuUup0S9J0IdsKtPgB9dq9fcbwmi8KeGIVnIBd539EKYEV3VXBPBZIuxlqRGfMjknREJLlTU9zQloKcsy0IDXbbzFjKJGycGgg8Fo/3CYefhJw2W0yGB1lhyNvwunYFtYhiKBjQcbHJoOvo5GbpeGcAbkyDsT25KaVo+xsXE6l0UH2ma2mqflUrBl9F47uB4xGbFC/4+Kau8E4RC9nJzRKSE6kdPLwHQmPGJIO2hPUFbc+t36zxxYabixDO15sKKxyD09Mz4lw0Bp0jTcNDffl0xVUVKvHJqeH+tx4TCTBrMk+xssB0qy2jJlybdpLRyjWvopixgHqiqMS5La0TMRSXgEjTuTgjmQmmXxnG0raNzIrrjwNAaltWjKg5OQjAonJd8PFH4dgiW6OlpznOPVZ01tZxjjoJPIS0K2EUYUWAp2trnNybFIOTxdtuV/QuMfNoyrosuVqFRSGavMFI2rVvfOfbvOhdsWgY9wa9jeP6Kixk8TRhnOaY3kjWGOjJTsaA8B28NsMEt7aDHvXaWUy042947261JAcFZ+9utSi/RClhGK/JorA7dKUUPdFcsZvUzlb06SmlxrXSTvIxqu2wYiqa2ohGLlz3/7D3brG2Zel912/c5pzrti/nUlXnVHdVt/uK3W0HBzuK5QhCBFgIZCEBgicIkRBSEK9g8RwpEhISL7yBAhKQ5NEPSBBZCZDIjp3Ysd22u91d3e2qU1Wnztln39ZtXsaFh/GNsfauLndVdYx8LPaQSrX2PmvtNeeYc47xXf6X4ImyO6TOspee9bPdjhfCeW/v36Pd91z/43wjfe4nPs/9L7wJ5IJWI7ly6x2hrCQx1J3RGI+yU24zAj4DmPP7QnvQCBygFaHUlMCoUPPgOK1pU5H1nkg7YXmqSCw2Y27JILBDq1ucbenlgdUGkDqMbRuC7HpBu7qbJG9rPqqTJinDrMuLx9wdkY4Ezj1foIWQNO53NKJr6a88m21euK6/9X3iMFDWsQePL/nMV7KU+pE7Yb+RgvDVFr0TvcnOoUUzwc0WhKlhEn+OadfgBHmqm8Qgi402hiQo1PuvPsTJVHzwR+8ybOFKhHy3l+ecSOuyGQ1xJxvE+pog0YBXEWWl7jBzxJMjfJsXP9NfEPYSBfaRwmezMTHI/dN4g1UztEjJ78w56TTP3+q1xySfazfrs2ds1vm4ZivHfJHncn6yoDk9ppWFPHiPL7LsiWIwRvJTrf2EcMAiBB/ANiB1seCnWywndeP/9fWNFeHTLA53NYW7cTfuxq3xUkQKH26XKF2IQ5u6Mqcp0hR0lx8ZesHxh4lp35OE72qToZMdwE+JnQB+TNugBY22GS7ZSfdgP1hUGDl7K+/O18MFv/DVL+bvaVuSK2KMH+BiDheD2xIEAZi0Z9YqhLdFPwyVbmuTzS0UcoRoBIgfphFCxEhElBgQsSN8mNBGKv42MBcF5NEndOlW6MTg93SivNSdnILUETyu1iG0iWjKFhSrXH6MgTjTtEYUmnxiuMh6FH0/crnOYfrRwzl2Lm3LGTx+LYOafH/N7/7WH1DYRu+8fcY738+ejV/4wpeqNkPXNOy/J85Xz865/7UcTcw/84Cj5T0sYh7rA5OQi4yJFZ15cfYCLfMXk8ZLfUatDEOvCQJgM8GzvsqfPzIKHSX9UTOU0K3NsCdd5Hk16YT2+AQl3A07zFg/FzMdHdBCvVcRkLRq3+8Ytz3dKyu57huUWNnjZrzyuUxRP17d49t/8E0Adldbjk7yHM+tZwqXDFtJLZJnCqX2ZWodyrimBAO5nVjTR4MmVo5ETOmPjwRUhQJSYYHqj29bfni8FItCPm7JvW2s0tnNbk8U3wG/76vvASOYJAU0q7BdJEkdQg2hPpQecAJtTjrxoBM9g+OOpbSg1gFmJ8fsg8iGXTzlu7/xmwC8+dN/keLnkFKHcnKDWY0aS7gfs16eTL4zGi/EqThFnPTsdRzx4prt1Rbll1XYxQeNNZkxOPoLtPTKJmPYlfZeHKsOpPKBxrYkYeapoxVGMAfROJy0sXTYEqWlGHYjSRaIYb9HpZF5ECHWy8iZmMXatsGLVdywjcRRmIT7kSht1zcefwXjVnzvvayxmMaR9XV+36//xvvYo3zj/4Wf/Tkei6DpG/OH/N57+SHa+p7udYu7L2ITWhEklUhe0UpL98GDB/itWPi9OGd7eSHXeA9+ZHeRF/K5SfTbvECc3H9YZdmn/Z6j01xrcQS8FK17rolhohGB1ckYrLhyaTdR8qIU08EPJHqsXaAFyRq2ayaBkEel0I/zQtQc3+Orf+7PAfDb/+gfcn2WF9ikBpg5RhHQaZyj9A5NO8eJFmYioGwpNB6KiVopkfMrmh7cePjjrYUgqUNa8aNUFe7Sh7txN+7GrfFyRAo346DgsQVwsduSxBdQ7/u6SmtuVG4VxAVVoSkmCEIdDlGjpYJjE7WYdxo7khSzwn7i6uoJRhCB6qTlu7/5+wB4Gt78WpbfCstjehFUbbxGCagm+pE4BFrKrqeJTiIAfG5LAjEEtJxnY2wGK0lEZ3QghA/k4wPIDuTUnFZ0G6ZhT5T0Y7Iae/yA7tVcEE0PHqCkdeZsw7i7kDl7wU4k18LOcvQgRyMhZYDPB0/y7h4uFVpclfa952qdD2w/eHSTP39+1nOvy7v+6SuvcRkSVlSPz8+f87nP52N59bUH/M4ffguA3/nt3+P5K9mQ9ctf+TInS+EexMT5+XOWggptj48wulDnHUGXgqpmNityej1xzJHG/uwC5yeUSNhdnb1N3+fw/147I+3KPEfGPqeM1gVR1YXUJ3Ce8Vqivdkx3Tynht3KsX2R5y/5oWozDEOPsx6hRWAnx14k+abtGc2YOxuxbVGzfC5f//mf5uJZPubzF2eQPPOlFDdXK8rjl0LAF1eo1h86EbYhSHu0ciIOvfuDbEKeuRuvb/Q967P1ySOGl2NRAJDQ1k4j9DkUDNdX6I0gxRLVwdfriQJHC3FgSIG2PNRKoSQpm88cUYRVxpBoZIJa07OUEnNQkf3FJV5aPy+mJefPc/X8G0+e8Lm38sX+y//Ov4Gf5XDTbvtKdOm0Juqhqjv7BpqtWMq5G2FctPh9YXkGcbYSR2OnMaIZOY09wRctRFMlwYNT+FZ+/+p9ptdfRz/KWgftcl7hr+PmnPEi34hx/YIoEvGzo3t4qaSHvSdsE+vyUD/d0qxyJf28v656g5snz3nlkZCm2hPOL/L3H7/ykPsPH/DaNj8894+OWbR5wXn+7gU//kYGffik2Yia9jd+/5u88WO5Qn/v5IjtfkcqC/m0rS1p2y6JRcxFOxZCtPrGP/stTkQx+fpsA/t1RSRqrWmF3HRx+R6dsEmjSrgikd4pdNFEjAqfIkpSUG0SXlivW3pWD/K87vZjxcnEy3OIFxgED+ITrSkaGgkj6QuuYZBnspk7jkUNO2rL0++9RSNsXqUXDGM+t2Qjx6/l+Q8ExuKgrQONsEx1a0FTCVLpVpD/oYe/sixv9CI+Bc75Ln24G3fjbtwaL0ekoCAWtF0YcVIcw0esGHsEIqoQbVxEh1Lh99hGZxVdILlEsxI8wBCY/F6+xGMEk358tGAh/ILjIXBkNM+elyr5Opt7ANPVGW+f/2MA7v3Cv8qlqDU1nWO/L/h+DcYyFRikhrQslNiIbIZYa7FiPOtmhhg9MYj/YLQMgpTTXU8jRrjj/phROhGtW+Fl1zj9whdJr73GtMrHE1EooY67MRFFtdlvIgtB1B11C54LWOfygz3jfs+ZRAT9zJKkIHt29YynUhzrlvCddzNVeGFe4f4ipwL9kz9gebTkeCWmsJ97jJPd+coaTqSqvzg+5emzHE383h9+qyoN/cxf+lnefvqEV7q8i7oQ0cVXM3a1q5HUHC9R3yuPH/Ps29/Lx//ighfvPMEKRX01h1krHZvWoArdPESs4AR81DSFU4JD4VGdYE36PZPNc+HmR1yJhoY2LUoiwmbWkMKsFvF0UsxU8aX3TOeiJk4DR8Vs1qBnOQJYPOh4xVvW7+bi7OX711ihmPslTIJNmZkTOolInWuqiGtUmb5es4B4Q/VZcStSqCXHxA39BT7xeDkWBXKoB4BJDNc5FLP9RC/KbpGpIhqJAVPE99CZNCUVa+NUdXr2vie6coMlJoHF0iqc1CRmJtKaBaciZX71/pZvCWNuqefEmNXkfvVXfpvP/FxmyJ2eHOEEouqHCU06aOyZRCjiGa3BlBDTUm/wlALBexik4p3uMYiAi1FNhe/26yc0rUiwNYYoKc4w9jjNAVocEmotxK0XGzZPBam4vWYrEuPm5ITrF/kB3101bHd7tCwyD197hQ+e5psy+hmX+3z8ndI04rD14voF39jktOTqcsuiW/HFz+c23F/6uY4H9/L75gasLArvXbzgZ372LwKwXg+463z+z//oGfdffURfhFGixdmSMlDZhyjPKBX62XJJK+efhp6zp0/RAnuPxyvGVlyv24hdZaTj/dNXMJKTW22q/N1+2GNcixHwWBoMTbHwC5GAQNtdoltIfQpLHFp2YkmYdttKVhvHNV5a5LM4Z27z98fW41sRyekcr33uc0RpcW7PX1Ti0mwxYybuWe1iiSqO5ratOgtJEslDjyGSJAW+gWbOYKcCg00RVTQ8fgiT8sPjLn24G3fjbtwaL0WkoEh0Yjmud5fEQUhQ/VghwylafNGDVS1iq0gza7BtIu0l5YiRIEQbbRxO1Jijp5qFehezgQugGMFPWCEJHL/S8fpVDrPfPt9wuc07wLf//m8yCWbg/r/+89hW+sppD2NiFBJOihNBUpkuNVXPP6QJX7CsOmVegnBg+vX7NIWcNM1AcALzeVOxBapx6IVIe80ckYARxJTxiZ34VmyePKteCUlpBtkgfudb38KKmnScGlSaOHuaQ973n14xSfX7YrPj9F4utH325B5pzHNx7i7pJJoa1j1XFx/wrek7AFxvnvK1n/gyAF/80lfRz3NE8cajR7wvu/GXf/wncRdSrcfTtB1GDGtDUoQSCVtdI2E7DQzn+W/1T9/hVO6R8bhl++g+00ais96hi0RWDGgBnF31Zyy6PGdGGaLcP13rcjheIi29R0t10AeLEXHWZCJXGykOb664N18wW+bU1C0WDELlj8YTy7Xsn6G3gqFpOkIs96ImKXjwZgaA2bljvcsR8eg9TiIQZ1tsU3AKgVEi6CCCrj4dFJZq9pBijQ50ShWmrRX1tfoUT/pLsSgAKMk3fQxod2AMJuErzJRjEsk0r1MxcM4EEuMJregZuIZYJdQ0+2vRZhgdcSwGtQcCUnQKt2zQ8vn9ZssrMoPz+SnXL/KDcPbimvd//RsArL/6VR5+LYfOwxiIsacRd+QpeIy0Kxu1YJLjTybiBJ2o0kTyHl9arMbWB2GKU+040LQoyUnN6WdQDzNAKd1/SAKUEL+mTU8qkEoiIR7SlOGFLLbTQJAcuI8RmobuJKcm426N0Ch4/WjGN9/KNYWTN75IEJ2KxcrxZZ3TnTdfDXz/+4EkAjDjOPAP/9E/BeD95xu+/rrc+FbTijHL/OQUJ3WDFPcYbXESsltjGMvdmxRd0b/cTQwCWOpSYJK6U1y12M8+4OK5cDGuJpLUEZyjdk/UsmNlyqxMDML+1MnSuhVOldqDwspD1W8ndPGSbBIPBPz0dL9mULZyboZppC1uMsmShBdjkiVJJ2ga9pjSkk6WIYFZSl2oe5Xd9/PxjMPA9ff+KM/z4ogkxjrN8h5GUrwUPRqqvoNtXDZWRuTfbwlAl1QhHliSd+nD3bgbd+NHHS9NpKArnJiqUDQRq1inazRKCk0xTXQSFsZxIilVAR++iYhtAGkyqJR3J72LGNHp165jJBfWpjFibCKKa3WMiU6KkLH3GJFGO3YnfHudw73f+JVf4aeX/xoAq/tHGDdHS8jeR0dLKe4oomDOkzrY18chOz4XBqTSLU52BOYrkpybPTrFLKR/vTyFlXQl9EAatijBHaj9gBcF62lKOFP0KFz1sjw/XzOkfPzrOHDvwSl/8O0c+Ty8f5/HxYH5jc+we5E7IWcX73MtEninR0tefZyPq1tNnJ78GGfneRe/3mx5qLLCUPQT77zIoCivRh4LtqJ5s2X+MJ/LOGnccolG6NIKTBHFDQFddBeeX5KuM6hrc/2csfBbGsNi2TCM+fNT8kx7iRSXHRTuglJ46VK1TYsuxeXFMTE1JFHfUkYTB0kn00AQOHe0nkuJWtrZKSGZuo0aF/Ayt62zWInC9ttIkuLiqC+ZS3RrZo6gDUFiftNZHr+Ro83f/ae/yfYsg6923Rlunu+T2VmLEVBZcgbrLL3wV9rlMW4u3bCjo1u6CTcAzx/tr/Ax46VZFKYiZe4DnSvUX8VslS/8MA1ECdEitrat1MwSk8dGiX/jWCcimICZS/VfR1RFMdoqo90oB9NEK/lt0o7opN2YNhzdF0ThZuChhL/P33qX9//+PwHA/OU/z+KVE5CaRtIeYQvT9Iog5+WHCecKgs2gYsNYEJLa46XK7BsHhdJ8/xHNA9EMaA1DKnJoibTdZzl7YBoGrDxU946W9OtcB5jGqc7r1W7PUBR/m8Tv/+53WT8T8ND1Cwj5Afnekz3jIB2bYU0vD+KsDWyk1tM2hqPTo5rC9WPP49dzu/Lk+BQnC/SYJv6f/+tXAfjJr+/5F37mX8rX7GjJGE3xZyVGcEIj1vtr9uJ2tf/gaRZDAYb9jr2gW13wGOMznwA4utdx9Tyfs9KaxWoucz5WevLkB1TRyVANRrWkWIhjuibfzqT6VGhlK7nMWIP3kSAdpJgiRjomRuusjwBMKpGmQtZr2azzMbdJ4+bHaJF/JzZ0y4wQ/al/8ed58q3fAeD9995iIwY4137kSCCUJ6cNnVngKbqW0Esdqw2zqsWYz68A+XRF0apb+cUPHx+7KCil/kfg3wKeiWckSql7wN8BPgd8H/j3U0oXKh/Nfwf8m8AO+I9TSr/5sUehFEZ6yMm0jJIfN8ererKpnzFJTUBFzyhIs5nw/YtqyrhWJBE8CWnEunKKkaQOF7QkYY4Vau8rus1qW7X5F/M5RmzL5ouACA9xstlw/nbuN199+wHtZ47oZHew3rOR62MGDYKoM6iqDmWUxVhDdyxaBVPPXjwMQlC0UijTq5Yg0u3Be9IkhKphYLi+xkxF7NPRrPL87faX9DJ/rmvQxd6udXRC1FJuxsUTWMrlD/2Gy7O868+WRzx/96m8b85rr+ei49OzMwbRoHj82kM++/nHXG5y5HHvwSmLZT6Xr3z5C1hZ4C6miJcd9PzFBW9/8y0AvvjlH8edLCq2Q4U9VLWndwmya+rNBisVvLPzNdM2P/jGQWs9M9FcdE2De0UW78mjRRhl5ebo8hBHjZO/Nez22OZQhQspYkU3w7YNRsSCVUj4QiKbtiRUzemNOmKQe2Pyoao9xZk7fL5PNb3XrSNOCgQRabRimERkpnE8/rHMIH3v/ffQ0u5ePjjhVDQ4FvcWLO49RIkew37o0UXvUWtsMaXVpi4ASunDYqA+eaXgk7zzbwG/8KHf/VfAr4iJ7K/Iz5Ador4k//2nfAIPybtxN+7GyzU+NlJIKf3fSqnPfejXvwj8K/L6fwL+Adnv4ReB/zlluZhfU0qdKKUepZTe/+HfotCCiJvsjFGAIMqDq1VpzUxQd71XWGmP4SxjP9V8fYoWV1Zza9HFfzKMBxUjZUDaPqGJ6JkqEHeSMyDgJzMzuEZaRdNEEhvye3PLTNpjbjcxpQGv8i6utccW7kJyrEQFaWggjkUxeWAc9oWVjWo0SKTUrlYsHueUQTctSdqgTR9oJALabTcwTRXwk0KszNmodN1NrvcDZ2LJtx93TDuJtNSM2XKOEfn3aBesjqX1ud5yJFO+jpojMZDp/cAffv+telzNUctKlIeG6awqAvVjT7c6lqs68SXRphgHz8Vlnr93nz/lc6fHRIloxucvcvQG7N+7Rl3mudy/uKCTSvxSzXlxnVuodAnTee4vc3QwBoUTAxZjUwaGAd4PpIII1Ie2pw4hy5ZJu3A2P2GSKGK3geWygIIik9D4U2OxrauGucR6ykRtcKJMDQF6kWgPE8bk44q7CaX3B4KcOmAPp3ZGJ8Y2X/mpn6ERINOUEgiorTeOK5bMpbPlZi1WiHPGGqz4giYUSR1MZg5mMv/f6ym8Wh70lNL7SkmVKZvJvnPjfcVg9ocvCglK0GLcjChw4JA8/UZMQLslai4FJO8I5cSnRKsXKCkuLRYNwpvJhSGZOD2zFIqbDgfNhah7jG2JRWzVgBWWm9YdShyeNJEO0T5kgxXPg/nslNB7RqldqJ3HyeKzdxNWyWIXHFFMTG3T0DpbhVixB83IxWuvwkJ0DkLP+lmeuusnT0mCzlveO8W2DWMRcImJ6VIEZM6vuZL++ZQ8a0E6DoNnsxMXp9bgNxOIZmJQptYHxgjXUjuY9Mgw5LTi+LTj9Co/7E/ff871es2rDzKzcNY5dvKdT568x8n9fFxvfvErjOKBsdvt+fyjjA7txwlMAmnXLuwJk0irjVcjXgqVlx98DyXCKh0LOlkEfZjYDS1tcQfXscLcJz8QZIHRVhNryphQcs8suuOMDpQH3E8DiCuVVs1Bm0OnKsnetAqrLNdXUvtKa4wu+eCMveg+ONcSBByi3IBqRGLeKYKfapphuybDlslw+ElSu9NHjyo5K45DbZW7RpM0qHAwyS0Gw6PSFYgQYiIIIjbEg47jn6Yc20d990cWQG8ZzArw5m7cjbvxpz9+1Ejhg5IWKKUeARLX8QT47I33fSKD2a99/espFaOPpiOJrXgYPa2Ehdq0jLaQnpqDMUsYSX4iyNpjW1N3VDWF6rwT/MCsLaKZiiiqR+gONUIQmnOYDsQpNY5oUWtKNCTpdTbGHVBv7YImziv116PQgpybwkCS8DMoQ0x5Nz1aNOz2WzSlGr4kaAmFvSNK+LvfXbFbC3jn3pzFg1zhN84SfMBvBIwzRkbpfvTrNRfXmfvgU6zqQOMYaYVoMwaFnY0kn6MI3ffsd3nOnl9fs5EuzXypuDjLgZ8bW+YF9ecMz5495VicnF577bP1Rup3O37rO78BwOPHX6Z9kK/f0w9esBAjlc4Y/NBzJWpP/fklx02R4h9Zr3Oh0Y89XgRpVTvUAqIzDUO/p7MFxZjwU5G2C1VnoHHNrVa3F9DrwIhxFi3tj2na42ZybZuBSa6fGg8q8PvrLaZZ0DQZQObcgrXPBVkdoHPCnUBXDYfZrCEM+VqobgVDQ5LCd3Shpg+GWCO1wTiCqDPtnl7Qigvu4Hr8IjGTZ6NRp/gg18Praix006zW5An51ONHXRR+mWwe+ze5bSL7y8B/rpT628BfAK4+vp6QR5Ewm5LBCpy3cxYTSk0gHtouKhKDFAFUonGW5Av80xPID1+YBpT83kYy2wZImCqTNk5DxkIUC2KT2EuV3ypfUWujNRURiQ8knW8CFl0WUJFQtgszxr50CUCZovocMVLJ3l1fMQ0BLzmDbixGlI7DcsFUHLJUYCU5vWvntS/tY0ZEKgkT/a6viNDVasmzbV6jz8/PWayE8TkFeun/X17vcHbkvug62k5xLiS0zWZPK9iOe4v7dVE7ufcql+cZs/DKq/dYhTlJ4NhaRZTc1VbBl7+YhWm218+xs5xZrppFlZE/eXBK9AEtzM6rt95ikHTudG7pRQ16rhV7Sd9cSMSCBdjtmBuLlqd/3w/EqlPgmAm6VHmFTsW41dS6E94SvCEU9yXjSTqfv7M7rGAB+s2ElprEMAyYwXMqCMc0bNBD0elMDEX2joQVElpQI6P4k6R1QMUluhGsTLevbUwTOvQkx9w5ktShnl/17Pu8cLcnmoQn1ULUnEY2EqftgQX54UWgbJ78CdYUlFL/G7mo+EAp9YTsHfk3gb+rlPprZPfpf0/e/r+T25HfIbck/+onPpK7cTfuxksxPkn34T/8Y/7pr3zEexPw1z/tQWQ6uK6vo+gJhK4jlYhg3GMFq07siKlU+0eRM5MCjDaMsoNqrXElLvNUySuvbfWoNMlhG0WgODlBMxTwEhhBUY6NphNlYzVFJun/2tUKNXn8TnD5+4jzQqv2I72o67TaoUYBnqhj5vNTdsUU1Vh0MT3pVozCKYhm5Eg0E4xt2EgxdZpGpvWORjoTF+eX2U0LUPMWLaH4fNHgpbB1suqIUrmeHbX026kWOkMbeCG28Cenp1ghjs07y+U+v2f1+isg18U4xcLN2Emf/up8y73jPDevPHzIg1dydNAdOZSkcqcPO7aibRH1CSZFTud51zxPA8OQd8Q4W2IkanMpVrqznRJjkaMLiX4aiGMx352q0aczLUZ4ENaY2suftx0bQX1qZzB6TkLSTKvwIhCMGXPbi1yjHiQaaNuGrrHsLnPgG5Oia6Q4GQ1FW69xpgLGxl1HFHRqOBtgB0lMZ1Z2QS/KVc38CCUU+zD1Na2YrTqut7no6iaFG5rKcZncFZP4pNJ1tKJtoZv2pqICBbL0aYqHLw2isSmGrUkxpeJa7GrQo13EC6LPqhnKl/RhwsdYW1rTOBGlEptSYJSqsJkMrinad3tGcRFa2RlT8PV7YlA0C3EySgrEoUppw2TLQ91AMUxJGmMz+QdELP5gcfsAACAASURBVEYq5k0zYYoYjNIkQQ2qZg6ztgKoknYYcWVSrquWdJ2f2ErI3u8HooS1zirUfmJznkPe6COhKe7Wcx6/ntuARxdn7Pcirdbu2RaikWu5CNcMUm8ZwkCU9O34wWkxumbvd5wcSfoR9rRLWZRtAt/gkc7KdmKQes/yx06hdOe6iUFUZhp3zPHRQzmXnna/xQgDs532BAmzN2fb6q7tgybIHA1TQJUUrW1hHKqZTtITyhQjWMNGgFDztkGFAv8esanY9PWopqnEsa7T1bYtBUUQNWiVHI2kHIaEHwaU3KdxUkzSWaIbOZqJwe9mn52dANtqttf5vDr3GnpIrEXrYhMSi1ce1vkMYjvo1QotaXL34JjtkK//evOUWX+JbnI6Gc2O7lQk/t0cS1lID1qgipv6C5983BGi7sbduBu3xksTKRyUaVXtLKQIkYOybVnuTOpxhUYbpvxfLEYrgUZgwj4NKFNWTUtIRRnXE2Q77L0jhlDxDIRUe9vBGLQuvpKq4s6tdRXD3rAk+EBIeQeY4g4lRbPGdlhdlJ8USReAloGjjiQgo3a2rLTqiEIVEdbtFmrRzdVOyBhBpa6a5ng/1EJTayydyrvWg88t+OBpbv5Yo0limLLbDZzMZgxS+Fz3kePuVM6zRZoiDGGgEZxB20UeiHei322xwbCXXbQ96Wiks/H06Qu0SNU9evPVWr6PscVL92L34oJpd0kSrYTdMMBULOT2tefvQ4/2he/hENYAjdOMaiSKcqtSsUYXCg6cjDBW2T2lDdVhOEVSXKMlfZj6RLTFnk1X5SefYLWUgnIaQEe8QOAbd1T1NZSJbCRl0Cnl+wkYrq6YFZBd2uAnSye6HWZ9QWgkIuheY5DI1YamFhPn9+5VKPb3v3mOU56giuqzYivKU8MVnNhyb1piiXoUFciUPgXM+aVZFJQELUplJxwAn9SNE7QYUeMd/YiWVAIdUEmhKK5EAddITmcgSMfBRghDuXABlwrufQfTBMVn0afa3gzOokxRZm5xwnjc+S3R5htq1cwJU6AcjqKt+b1GVWEPYwxR8kbTdaRlSyudAdPM0YK48tOAl5aWCh4tyEs9a/GxyI3vuHq2xgtgaNz2dIJO3F6uMfogVbfvhUlJ5P5JDj2XC8UwQZDOTL8/5brNoWxUocp8nU3X9WE3quGBOD/ZRYtzjq1IOFyES1YPc8p19OBVksx/chONcDdmjSGWTlBKxHHgWARL+MxnePqdLNiyvr5gGHN9waqhmr0q36IF1DZcKZpW4wt4SytsdcIa0VOReaamL/sUmLmc17Sqg2moUm1N0zJITahdnNLNcn1kDJFJ1LBD3NC0CiOLXBr3tFJj2V6N9LIqz+cLdNGY9JCEvRm6Pck4gnyPUYahoGDn22rq68NQ6wvYwPGDPK+PPv8F0n6NFvPaZrXCiLBMSqp+JrqG+FFqzp8CvnSXPtyNu3E3bo2XIlJQUAUqldHIAp67EpUSqmvP+MFrbxBEJu3y4jv4fkcnCsppyF4OAMbMD+tjH1ACCvFDqGAX7z06BjQF9jxQVtWUuuoF2ShHsb2O0w4rqUAYLtBJ40sU4rOyD2QgVLhhEV7s8KYpEoyhkZC3UZFJ0pHJamJ1kG4ZBf7rd2s2W6EOO0Wj+gqB1coShLwxxZ7rda5Y96nJcGLANTYXToH7915BmxkFSbrpRx65HEWsr644EYv374S3eeMzGTDl075iFhbHxwxRcf9Rji6adB8nikKL42OCVO+ntKn4jxR7GitKSyfHXFw8ZZCK//WLZ8RJ/D20xQrk2CZF3TRtQkl0loJhQlfqMkkxSRHVqcSiFHBTqi7PnkAo8GGVaExHL1ySlKjdKMY94UKwJdYe5M+mnv2gmM8lzUuRfiqsza4+SZMfD9oM2mJmAsrSRQck3zf77abyd168+y7zovtwjwqMi1rjRU9hfu8+cTypUWA7m1fvh37qa5qmXANFp8McKN3K/JlLH9QBeabAF8ENlSj4rATZ24+sT7e6l/PbZh64unifSbT0CAuiMF+maaiCGy7OK3hI2eeE8jp2KJXQ+mBQW4BQUVmMLSjIsS4wjU41fYmbK1LQaOHmq5AOtn6kel6YDl1aWMbSTzv8Jj+UtuuwglQjtjC/J5/pmZ3mv7t+/j62tC2tZXXSMQn4ZztMLCX8XPQDTuXPN9Zx1RdtBVhvpdXanvDotVdZiv/kenfN8Um++fo4MBNjmEdf+xJ98bIkspAWIkrRoHAiALPSS7w8sGbWMG/z96d0H1+uhY84ITBtxpGoFrzzTpYgO52t2BSeUYxshaOhxi0zqe+0TuGl4+SBebesxQOdNFFyfWcNRjoxfhxoCiU6OfayqG+nLXs74lVOrXqlaKVdu9/vGGXDsLNZJdc12pGSqn6a1rna8Yo2EZu8YIdeYZMsHGGkKzUtr0Eprvt8zV07I/qixbnCCt+kP3tKm29trDWV30DTEMfI0+98N3/GLmiEIBiONPZEkI5R04lbWIwRX8hhfZHr+/jxUiwKP9AuKUSTpEipLAoHQkuMAS+FxmZ5yqNVS3+dT3pzds3lRdY6aFcGvZFW5faI4kKlzTVWFhjdOJJP+aKRIaNRdl3jNKkUC1DEsewAqgplGp1AJZI4SHujMEKomcbAXApVo9EMQTwoTBbuNAWtOFuiTHngLCHlzwSrEQ9Y7ClYWWCMaaAfmHW5uLnfb7j3KDtZDZs9GxFItbMFD07EJTnNeC2KjP3lhiEpVq9lrYRu/hAhqdI6nXUvAZsajqZyi2iiMPycBj/0NU211qCKtoGegWhTkA5tYPyOWIRjtxOtammOxWrt7D2OluKaPe4Yp6LXmUCYpdraWgNRjWYx6+jXonHY73Gm6HoazkW7srGKKIKoxljK85VCYByHunsGZZhkUTdNi5IIrF3NGAW1ufeehZ0RpMaA9QeWa0gkuX8MMO+KMKQihuLKFUjG4KT1axtVW+yh31WtisXpEUrMjpu0QAv835kZ7mTOmf1+/k6nCWXBMRYldSyfAteiEGbVQda++RQiK3c1hbtxN+7GrfFSRApwQDQabfBSVLgZHZBSpYGmdJCcMqbBcsKRiAAczXcYkXU/f/EEJ3Rpt/L4XY4A9tcJU7FekZgmogB5TFJVjVmlQJSK/+g9jcm7GToxO8pV+f0wMvkNTlpaMRii5H12ZRlEbj4Ygyqa7k5hFzNaIQil7oggu7jS4Eob1CaSGJI2ywcVqdaPO9LpjFZANvfiA6yAZya9Z9EJ8lLfUPkNGhfz320WJ/TTjnUQWnhq6k7XNA4nVW2dmpo+oQ2DzJkioU1zy7KwSPErLKHQeBXE0uqdJoazjAZMT95HnV9h10XKP4IvKMw5T977NgCrdsGyyOTt9yylwDCN0J/3tWPjrM2AKmAbIkF2V4ypikTEhBdpOULApESQ1LJru6px2Pf7rKmRP1S7GhHLfjMwKxT/5Gnleuz3W4KY3c7aFUbupejHev5GL3HzBWMslgMeX+TgsAQjiMrJw0Y8M/d7Zo/kWiwapmR58Mab+Zj1DCsKTYMaidKGt65By2Ntk6lAJvMp9v+XZlGosahStT2ZUJV9GJOv8NEUDSoVvcPEFEeMoNU0llde+wIAs+UJmxfFqficIOjEdtWSdkXddYdzvpKQlNbVkVobgxLh0e7IYISJOGKJRbCjtTS2IxSzUD/RTwVO26BEfEN1psps2WYG5hjlckFv0quK09BxIslCpmJTiUYpKZQuIXpCqYgT7wkVEmO56KdHmNKqU4kkLdFxGBBJQ5y1OGvZ+Rym3lscYaKIwgZQkrOMPuEE5o2PBxcsq3GqOaR9SVWzXJKpoSwEtECRbb/l6t3cdrz4xjdYug4bJE0aRzbrIm02cP9+JlS1RLSkAsY2TCKJT1QErVDCZtU2oqT2YEyGIAPoiSpca5SiKwVIp9lFTSzCGzpRVriUFFrObNzsEBApahhRWJQUsWMYSAKoyZaHgnyMlo1gS45XXT2WzWZD3I4sXpVFXnVMe0mBjUUJ2StcKvw+1x3aU7BiJ+ddQ7RLZotCyFKHc3MaW2pvk69tU2eb2l4Of8JybHfjbtyN/x+NlyRSSARK64pKQ47JHpCOKVTceUoaYYtjdIOxFiVVJKNajJiM3F88ZLXK9ufX7Tu89/1fy3/Xj9iZhKVTT9fM8VIcjJM/FDoN1cxj36/rCpq0YiOUVm0UFoWWzxPAUExrGoItfIEGJS0027S4Zl6Rm82USpOEqBNRH7DrN4wVa9HV6CYb2hTonnK1DRtVQJkS8oYqHKqOHN0kfo3DBGHBMomSlJmTusLt54ZKsULFsoPW7iwpJZQyNYoDTUyle6QOYrsxVuFUaywr6Va8uHzBdgxcFMt413ItO6JWI04iLb/vq+JzSFRQTjtbopTLLknAMGxpY8H+GwYx0l3en7F7IQ5RratW9D55xn5XEaWDs1XgVyvNtJeQqucgw29gDLryEmJKeCmCuuWCKO3OadjWSGO3n3DSITBxYjFvUH1pS1/SajEH2u3BS4tbUyXmpzgRBJFpWgudwcSZnOeMt5/kyKubO7TQtWf3TvCuoDgzQfDTjpdiUUikKo2ltK43lQqxhqhKaZLceGFSIEw4zSxXWNXhbxX2X4yQRPvu/uOvYERP/8nb38NIhTdtHbv9Bpqi+29BWlIhhdpqnHeWyRfF5YgqNQitYYo0goGIk6p+BtOYaJBFIVhKiX8YE3YeKTBIha/ht06qLnApT0iZgdqmSSJEXNfLaDGqyAYnKuY5arSAg02wVRpNzQw2Rpp0uGHk8IlaE4uEl9I1ZUj6AP/NaUn9KAp9SPOCx0rerPYv8KJBsd71dbF3Rydcv/8OyOK1316xEX+JuTOEjZCIlCGV9qJCVDFB73csmo5eEIGtVWjxEVSTphXNgqEPqFYg4+nAkoVEirEycPt+ZLjOacqiWzKT1nFMgZEibTagYsOuCD0aXzcp6w1Nuea+pxUswjQFRoHWzxcd87llHAvWZcI04kSlekZBXrq2ZQzl9yuCkMZs3BPCrnY8aAOzMn9XawyirbCY0cj5xxRqfeHTjLv04W7cjbtxa7wUkYJC1W5C3gIPhaqyH+YuhCASfSauAEwa0hTrmaQYq4LtzRGTxi5zKvHFr7/O+jwThdYv3sFfn2GKz+R6TxCghw0RBA/Rj2N1G+pSW8lIu6sNjZlXEg6zPSEWlWVTvSowroZybnWMbleV7psSqCIBdkPENiWqGUn+ghLNpDxfJTowqa7uKhpJr+rkyvxRo5G8qweihPZGp4NluZ5VUFBSCi/HHJWuqZxJk6BNBUOAr2Sj4eyCF29ngA39Cx49zqjH1eyIy2LjrjTb3RbElUtby/o8S5u1qyMKYb6xOkNUgWSpoDAVA37saUUs1xpTI7qkDbrwAJRHSQToY2AUIM+smRFjoik+GDZxJP4KSWk64W70+8ggOJfkRpbMaUpnA1Oj2+AhCYrT2YZR+CYoUzsku+stk7/GSZoW/ZKkBYXaDxgpIPbDiFGSIgSLlhBOjRM0I0kiv6gSnSiU9fsdqZoODTRLSSWTh0oj++TjpVgUPgxfqj/FQMW5Qm1Jeh+YpiKqoohaV/cnpRSpGmweho8WpQRpphTdaQ7rTbtCte+wvc7QYK3eRwkJid2AlZsgjV21CZumnk7nluSsO4YIIYmAiBpJwoRrlkewzOkLiyO0SK7RLUluXk1RQaGKXnjOGerrWkdRsZ5XShGdNKnWG+KNWYv16cl8evn8DQsxkwxKOSqenB6jCsLz8PVRFg8AS6gLj0aDMpWsFhLYsiqsLwhvZ6TiuP6AECVNO37IdCZ6hZdbTldHFaHY+5E3PpuBVDqlaroTx4GxsA+VYzUrSK7EtB/Q8pXO6qqH0YdU6xvGmNp92ccrfF+g7ZHVfM4kC/tAoJFOBtOOq0tZvNIC15zK/DkIA6kwOFUgCUxZOV2FYayKzFsxoR3jYd3WFk3CynUekiJIKmIbRyto1cFzUPmeUn3Y436HXpxUgl1SkVYQouo5xPGwKBxMZX29Z9Rd9+Fu3I278aOOlyRSOIx0g+2ZlDro1qtDSpDSAdPtnJPo4KPelw4/q0DZ9WLUqBKizV7l8RsnDFLQefa9XyPucigb1cDVMyHHpDlG5Z3eNh5fwm2ABDEW5aN7VcVItzOUKEiPSVflIJ0cIRpUES5Q8ZAKqBt1QuKNgt4hGjh4BUoPOiSULmrGqb4vg7/KezMWPv/e5sJtSVNUV7s/NyuY6rBnoVCVexLJKV6Q20cDqc+FwunyjK+/ma3of/Uf/A7fO8tRw6M3v4AXiLCbnjOOI4huhCESpLjrrOXQijEYWwp4kSg7faMNylrmEjlMU18h0F5benndWejkHBfMUHL9T2YL+qknyjnPlMNv5fyTwRfIewzsRMWpbSzORWYu33eL1RGXJWUKibkpPiShen76oKrHp1aQekgCAdcaQrnkMRFFN8Kajr7gcaZIkk5M7Pe45KuNXVKJRrgoxtgKkgrTlKMFwM6aQ4r4Z1FPwdRnQh/44PrGE3Kj7pBSqotCEqTjzX8rQ+ubE5FwKYNCInNScV9RkaAMRmSuTh/9eS6e/gEA/fYdjh5K7WK7J4p4yDQGorR9mqTRIeCKHFiEpIqElyokReauxVc1anKrTx5kyRNuHaucMaqgOD80X7cXQg1Fb1Ad5iD/v/Yo6lSGFAlotCA3PZ4oVXLSVIFh5Xs+PBIxt20FJKOjYiEdh7P3vsvufj7/xVFk/EBISE/eIxbxlphbv2EsupgJJ2maHVXVMVSqpZcUwyhHI6lk6keaxrIVqblx1+MkTVNOsZT0bakdIqbNlDrGNv/wbH2NNabWFPQQMbqYqRi8yOnpBoxoJgy7kUUDFym3osNmTxSOitEtQRiXm6mvbF7rHAXlZnVeiEsb1ZqEL6tCSNWcqG3n7ErtZQw1fUjDANOAMuK2jicJcrRdzvHra/mMPywK3eKA1K0aGx8/flSD2f8G+LfJd+JbwF9NKV3Kv/0S8NfI2/J/kVL6Pz7JgXzUzZdH+oH3hBDq5H7U5/+4v+VFEjv/sxT24FZ7rVvd5577SQCa5QkX7+UFwnURdS1Q3PAZNqLcpPoJNe2xcsMOfqwklhCoEuPb4YokFmJt6qpz1A+cbbr9+NdA52YxVt5Xdn6tDzgB0g2WZgq17ZmUPyycBPL+LJBdow95aCqFSOrfLt93+H6FVdRdzMXE9fMcXYX9lmd/lCOCo3bFtagj9esda4GM78eJNqhaF2qbhqYobE3p8CCGVFmWrmtIgq7chTHL6sst4BYrTCttuNCTivS99nWDmULkbJMf6Fk7w7VNjfZa6+jXQkIyjk48IPY6sBF0odaO62BwQjFvjMrwbHLhs9xEWpuqvIQ7YCmsLfeERDvSFgVIMRF9qSnF3OYm1wQQ/EOaJuJ+j26FAZkCSaLLbjFne50XhTj6WvsihVqD+DTjk8QUf4sfNJj9e8DXUko/Cfwh8EsASqkfB/4D4CfkM/+9UurToyfuxt24G39q40cymE0p/Z83fvw14N+V178I/O2U0gB8Tyn1HeBngV/9uO+5mSf/cTt9+X2M8Qcig4/6zA/+/hNo2yoqCu3R8ieYhB8wbt7FlPxw/5hZyDvjsH8fpVIF6TSzBl9SIWcrz18pXQU/QKG1qlHALSDQh+sjpQ136zQiENClY0CshJeYoEiUJ/qaFlhzEP4OPhCDra1LYw5tSGPUYQdLkIpOhHKH+oLKHIHSOk4hVFcjrWaEbabutloRhPvQaY2RSGEat5y4E86nzEuJ9oCWnCmLlhBAK89KIoCAYpBugV44bDKYgrgKsSooN9aAtI53621FgUY0iyZHiq1riSmjHAH6aap1oNBotHBK9BhYSCoypsSYQInNgEsDpjhMDVOdW21MDd/hRh3nBqEv/z5UWnMKB7ho8BNO0o8peHSNFDyx31f3MZ8CUVrC7WJ+MBieDulDDB5d0K3pdmT9w8afRE3hPwH+jrx+nbxIlFEMZn/oSAk+qlB4++dDTqyUujXZSqk/Jsz9ZOPD729F75Ew57VXfwqAy/Yee5u9EaI+I54/k8/uCdrie0G3WYsRhyI/JVpxrbapo9oeG50JThVDcDst+MhjTIniLZBSyI4/pThJkkIqoHzO+cntzFJgSjHW8oK1lpBsfRBjHCk1iRTDoQBJcyA6AYfAMhESVctSbTb0T/N8LCNcb/NC6o4dNPlYtpsLJvF9WHUN0U8sxROhaTQzmQObElHEUGyKJXrG4Oo19imx3a4PdQgcSgq9m+stxwtBu1rDKOF61GAGgR9vd6jW0ZlSKDUgC35yBlwhN1ELoEYpphAIcm8k9jUl6HfDIZUz+qD7EUJVPoox3qpxxRixwuycYjwUcf1UHaqGfrqVPqT9vsLGUwqSBmbYfCMoyn7qSaU9OY40wngttYVPMv65WpJKqf+anJz/L+VXH/G2j7zLbxvMnv/zHMbduBt3409w/MiRglLqPyIXIP9KOmxvP5rB7Ne+duNPqIr809pUW235VPnuujJ/OEW4uRr/YNHuJtjn1rnc+rnIaDt6lrNcWDL2Da4lXHxx9R6jOC8prcAZtHAc7KwlljBXD/gglODxQAPWao5xsSocqVu2HR89ckH0UOi7eWqJG6HpDRrzTcBSnteiMj2Tb5XUJh04JjGqSvrJEVjRI9BU6SKV0FExSTrA2XN2330LgKP+Kjs2AZdXa5TwLUY9EFyRVjMkEnPB66OGynfxPrCXeVLWHHD8Y6gFvHZuYTGrBcXGOKbyurFMUsnXxoJEEHM6WOXzH0JPjAovxb3OOYJIo0XvMVIEzl2EQkPXqJSK4BZBG0yRW7ca74tnaFvnLAR/K1K4GcWGEHCSmgwhVs/U4CeaeYnuAqkY506BNI2kcg8tZjUliCrSCrpxOO8zqQ9BN84P0eUnHT/SoqCU+gXgvwT+5ZTESjmPXwb+V6XUfws8Br4E/PrH/8VUb/j8kB9gvrfedcNZd5qm+vubeXj5+eb/P/z6owOawyhh9U4HFpJTt92Me6/m/vsH73wTvcxIt7af2G9HYittwM4zjlIJ9tt6EnbWYCREtzaAmkjCoUcZCB+noXd74cgLY36dUqopg1bq1umlUDoOhlgyDBPQpjn8W1KUoDHfvPl92sKBpnpwUVIkUIljeZCfvftd0jY7SO/Ha5CctgsHVyfDgnlhKXqYwlQdmnQcmUob0FrMTMJ3Y3GykEWfag1ExUg/TViRsFPaVkRmaiAUOz5MZTlOPrAoxrO6J8aEL8Qt3dXzTyFWqT2lFLrgD1LCKFUXpqBtRUveXBTa5rAo+BvXtGxiZYQQqkiLSh/uRBz0JksqpadG6goCDZ8fEytmIVbI8/rFOUnqWxndWGoKnzx9+FENZn+JDKr+e/Kw/VpK6T9LKf2eUurvAr9PTiv+evo0S9TduBt34099/KgGs//DD3n/3wD+xqc5CMUPwync+ttAKdLc7vt+uuLiDw/Xi4J01OmAQlSOQXbKz37157h+7x0AxiffxY3n+PL3IhgpRhntcMLTjynRFGU5At57jCkoRHXL/+/m+d4EZR3O0chcFGDTjW6MUlVDwQeFKqhDozOKEMAljD4UHrO83eE8D7bmqiYgKsWabkyMjG6gL1yG6+fMBNFo+jX4ooztaMRi3UyKa9nlbKtJJPZCEZ7phJPULBlohVOQIihfQvRYUZcax7F+teIsIp5ASc0UIRX8BkV2Az94TNGjUJpArOcWYqgkqhRHogDjlHUViBRHj9a6RjcBWw1qjbX0xZaeg4ZBmuKte/Zm10wpVe9do22NaHRIhOI/ahuGD6EbS/pgkseXoqNOBx6E0tV0J44jYSppTeHZfPx4SRCNh4eCGzUCrfWtsOvmA1IuqA+BRKq+EZ/KSfOjjuTG9zcxoqsUvKNr88TPHn6WJIrPbz/9Lu2yw4jSc+oDWvQGJx8wIufmZpb9Oj8Uxq6gawny8Ch9I+WBGySmw42bVDqE8ilJraV8xh0cttCVZNro9pBLqogR1F0IUyZVFXm1G7WDmAIhFsCTMCDJwXUoiscxstolrt7OwB6zvmIhYfb5foMX9uIYe5xAuWMYmXXSPSBH6I0cv0ahy8MTwBQtydTVtGBgzSQMKK1MJopVZLapyMEf6F4VwReTW3wAjbVMeqwVfx9C7WSEMNac3LoGI4uCHyaM1pVpGWir7oTTitIT9MHTyIaVgUwlxXDEGKtUGhxSCmsiw0elD7MZ+9LenCLJe+Ig3Sw/ouR6RkJFN3aLOTuBk8cb6EbTFtffjx93hKi7cTfuxq3xkkQKiYNumDlUybW+EeJGitmmJwNmIJNWYkq3TuTjg4UfTB8+ujipCU52gBv1Oxs9y6MMN10+eMz+7G2M7Bph3RNF4FX5gxnM3k/E2QG7r/SEFSFW0yS0vPbxEB3cziWgbo3KZwJU0U1I5vAaqs5BCFPFLygVqYZKKQo2pEReoYbcvqIPwJmWQejdQbt6iWa7wPk/+xbju1mdeeZ7EK+D+68/5lzk0OKuZ68ztHhYbpjEjMalOctuzlzEdn2YCKJ6baJGhVL0PMCDLYa+aCuoIAKqB3+FAgTKeIByXvFQtDaKsRQDrWOnRuKNaLMR4lqaIEk0gKJ2D1A5FisRbQyRYMqxxQpk8n7Cye/VDchziRRu4nFK+tC6lAFM5EJnkO9vVCq3PNF71BhuEaR0U9yqAknun3Y5Z7fdyLkcIoX2Q4XOHzZeikUhQXXbAVUx3UnFWlW/yfgj5TANhDuYDq03pW4g939omeEAivqBf6kLhDkYfqi6JjFNiXaWuw9vfOGneN9pdu/lB0SrDUpyOu0hlvBt5qqZy7Rfs98Hju6LRp9V1XTE2OZ2mnTjJjo0bWXBTIf3FS5HSuHQiVGxAlxIkSA5fEwBhakkIIy5YW/XVvASqa3kKJNAScrUrK9oEPV6iwAAH/9JREFUN+f463zOftwQRNfQ7gOdtAenKWReBRCDqTMe04RSgUby3ODD4WqoSCpALGKV4FPKVFn+JMCtqjUR1aH1l6b64Pp4eKjQiknMaBbMbiFEQ7op5mNqypW8l8IEGGsIPmJu8m/ESDamAVsWhXEiSaiutaku1cCtReEmd4UUq6lyigd0Y/QTTs7Lxwk9tQeCVL9Hrw7oxqIs3d1EN95IHz5Nvf8ufbgbd+Nu3BovR6SQOPgokG4VwG5RosuuiapkgBA8IcZa9DHqh4YHN0bZGX7wX25+v7m5PasCK3X18+1Rx+LBm6xf5KKbd66u2skNWJeLi02zxE9F0HNNu2wJOwHpNA1qfvN4bkYAHzGSlQToAHNOtR891v65Vs0NFapIJe/ZFu8DPkl0ol1pZ2OVRgl+YaYUSt7DsCe8n7EIu7e+Q//kLWJf7MkGovAtNu1YTWvsqLGiID0mTfFmSClm7L/Mk9GaWCTgSAf4tkoVM6KUxUgnaEqZ93EzUjCVL6DQ7sCJqNoQRtW+foyRRhv6ejxUzII2tqam0QdMK0A6owlTqPeXj4EggLXEgSXb++EWnqaCwgTyfJPZWkYIoX7eh1hTieAnrBMPjiEzJmv6sM/6Cvn7Q9WGcF2DFQXyMY63gEyfdLwUiwJwK6wq4+aicKuqnNLBO8/7PNE1zD68N/JJeBCHC/jhkT8r339Yk3L6Um4i5WlmDQi5pjldYAtd+HpfHZqsi0Qx/5hp2J4/h4WE5p0jys2XjKoS8TePPWYhR/n6lLH+taYQOfAisvMxZPBLBTpyaE+mZCFpVMH7B0Wjip5Bon+RW41nz88QZTHaxsGlmNVu3mM+JS7WksIdTbdTtdL+UAfSlFEWLwuHUol4QypMa125C1lj7dAxKYu91RYr6cOIz+lDlbfmhv7kgQaep+9G+iXnO3lP01iGuigkfMnvjSOKhkPyHsRg1ljDxFi7XCkEwg2yVdHr10ZVIJNumkNXJ95eFG62mGOMmMqDuJ0+dDPRjtyPuS0pKMw0DlljAVDG3wYySXtyuhoPPIg/k4tC2RJuPJsfflDrApESsbSTJo/RumILbkYK/297ZxcjW3bd9d/ae59T/XG778zgMZpAEttRiGReiBWFSEBeEFFigQPiJShSLBEpDyRSoigCS37Ja0CAhEBEREQkKCQSggg/gBSEELzgiMSxY0fOx8SyCWZsz4znzv3qqnP23ouH/Xmqu6d7rma629JZUt+urltVZ9euU+usj//6/3vmpote56K/e0Bg5xMAaeQjqu1qYjTJf+U2sLm34exx5lDQgYMsB2e3R4QsW/bk0RuodRweJYUodjvOnqQDHRxvKp+BGkcw7QpaSwUoVhesB7VoaMRibWmvzfW2qmCyAjNq0nsrsGXjcPn9zA/eIL6SphftK19izlOOZ/ef5/5JFoQdBDO6WpOIQerEnhHTIhhjiKWYqq6LwAIhhs4pOMR3TqGrKTS6eZsGl0i+MUrAmtIuNnWa04rNNYLS0i7nldS65BRmDjlEMnFqjFqLfmbc4AtOxTeehCQ9YChOxopUmHVwrn4pnbPVKYxD5xSCh+E8sjF9ToGx1hRapBBDrC1NZw0xzJgpRyfzjGbchz04qQLHUbRCnh8/eEDs0Y3XtLWmsNpqqy3sTkQK+2FeAy/ZbohJu6thiwBCCPgQiLYhx/ouxWIk6IIR5RIA1mOKKQXnFp7CIn8QoYJQIp7t7gxM1kW0FjlJqcSRfQ6XK/bT08dsnybUn1Hh8PAQySChs9e/wkFILU47z3iXkXJHJ5g8EOQ11lkHayXl3T31XGnJiSXONZNF8tVc1BCLoKmRJECTwUBeDfOU3vTTpztcAQJZB4Vhys/43L2ww33C8CpDHjGevSUMpY7RZjLEGDTvq8V1qWD63EpEYU3TDxWkUqerhPoZRE11BUjtyUCsXSoRrZGCsQ5f5gWsLKKGMh497zynNPHVtJJ81Y9tz1Tnhm60DussPufoZZQawDvHmBmurHPsznp0Y64VzLtFx+E8v0Jhg25zEPTcjW5gCr4bkPLEgm68H5grurENR6HUx4fpqtmaZnfCKaDUYR2c7Qo1tps4u7zPOk8TMRdXutrSMhfoJgmVzkFUIGWHjahP7ycwW9sPpBbDBMVZUyGnxk3ISUEqToSMQJO4Q8qg0PHzGON4+EYq3JnNQAhJNu3+8/d5lOnqh+PnsceJSt4dHKIl/A66cAjQ/oyqlY/PWUuUBm81BRFoIrEGyXkvcmhtxwEtsmPDAZqJS8XPlZLdjafE8XXGonr81BCKOl4HBcZ0WhHi6mcp4gGtxCjGNK4E0S796Ahto5qarthg8Oo7DIbWMoR1tsE5LkkT1SR0Y+F89DK3i0wMNeWK81xbgKZAnnM4bk3jaggc1IuPtVKLviGECpMWMcQQsB08f7/YCGAHZeohzxXduGE3dfwK3qNZX0LiXIvLSsTkY2yOjjjLKUahgL+OrenDaquttrC7ESnQQYm0Fa/RZXuQ+pilBtQcQi1IaR8c0IaG9q3nb7DWXNgGXdoiBKmXZlU4OtxUKfpDOUBzJfkxb2DLSHBUjg9yimAi27OHbMrrTdtafT/bPagFwPjkTbj3HACb59/LnCOAYRxRa5ilfHwxkXwCTx8+rGu7f3pKLO0PibVbkQaouio9WtuoZjMQxhwpjAeU9ol4XyOFzcEp8yAc5IhCvamkUogsiF9LLqYSalfFmBmR1JZMexgryMl7WzUmkVAjBY0g+QposSjTgmGqmkotSKI9ulG7OQhh9nONFM7MXIt7PoSaFgWo6YMV6hg1pKtpZdqOSjBF4HiJbnSutDTdIlK4HN0Y2YWWPlSW51zkLASvMoVK9afbM4w7zk8JRGlApu1ZihRKxHMduzNOoQ/z+6/1RRyLIu0DgfwFX7TEpL5kQ/ftHa4bukritVd1KaShA7FQQnRx3Ds+YsxMw2zPMDGFdeMcEp85YKIQMqJu558g+Cp15rfb2oYy1mCLI3vwCvPxC+k481MePEwnwfPf9C1M95/DjGWyUSvewrqBkFmSvfedhFrDdhTG5vKODYovmIHNyJxTMTce1C+12U3MhR/AjDAeomOGLQ8jmtug0cT25VPtnELESmu1ilDTjDScVWofHf2bNMbiBP4u79dh1TTko4TK9aBRaprho291A431MVhhmjwnGdos0mh3fPSZXwEQ03gOQkCMtKnJoJV1OYaALw5b54VT0CwqbIytThWWXYjz6MbWfSjYjMTdmDoQAHToxnh2hjnuh6My5Pn4CHk1H29NH1ZbbbVntbsTKdRQLFIGDtLgTutZ7z2hPibGyFzCvKFhFvoR6976FMFam/9uoVxb0h6QipaiLF/QcfJcuqI/feVPkG0iLpXdhCnz7EbZ5VQi4BGvjDlPcurqFcnPTbjjbH4MQwIMmfkR+jRdjXePH2KOjtEy4WSpUYB1roac3ns2Y77qdAK9mtOysrcCFOimGSyS0weGERnKoNCEzOV1d9jxlDBkgtZhYDvngvAm4qQoLMUKMosGbCjpQ0I3lpBZNdaU0YhBenRjB2Qq2pkJ3WjxRavikjmIdDtHY6EdQ6wQYqgzAoMYQklTlApkMmZAYwMymbGNUscwd3MQkVB4M9hh8+cybVvFX3LUsdDqyLf3x6mtKZ0Q7Qhdp0vVo3T7FFdZnn1NBcfjg1Y0Dd+A6UOs4A1f80voZc8uplyDZfgVYstVDbS0YY+zoHzBhdTa0wuCpn0q+YaJbu3AKImY5P795BQev/pKGyKyIzZX/IPfsssdBqMW5kjMua8RQ+4c4WetnQDDUPNGP205yKjJ6cljjvxch8gUW9fprKOcivM8c7hoqxYEZC0AlB1s2ZVp1OdxHFJdAdDtEyQ73jls2YynzGOiuR/HkafbHOYeUp3d4pgdzZ4VizG+AotC8BWIZEznFIynMVZ3UHbjsBimQnLSoxsjXU3BQK07pPdZ/7BSLyTjMLCTMjik+FyfGa1rQ2TeYzabNjWpUxNtCZ6Q5eAi4DpAZwMyDRjj6tRk7xSSkywOJmALbV2ISb2XlD706lGFXwHy0N1c0I1HHbrR1fakf/yQ69qaPqy22moLuyORQgfCMa12rWIWkcJFDM4aE7VZIXK1IlUYJWke5teN1Ep08tKZQSeaVBjLBS6/F2VdNBORRnq7oqNYju8l1ufHu4ljV9iGHMRMLqqBe1kOze8iQUNl403MR+V9tqjHmpGYi4bztOXwIA3HvP7oMffmuY07p2em5zjXQvYQ6my+jLbbZVnSu6m24pbVGimEYcBk3QCMxfiWPhwdv5ddHjxyw4ChVNWnCnlOr19gxqYWwAwOkamG/DF2QCZrkbmE0w2LgDTwlstj1CX8b/wKICwhzzVqFOkg04JaGr/CsMHkSCHQQZ6HsSJT4lwGpnIKY1oEaUQq7D1BnvN7caYySw9uuFJSDgrkOa1z2+tBhAS5bsXNGTvl7k+WlAMwm5NOrn6ocxBPHl0/UrgjToEFGKcF6csUYTF91tUg/OzZVVWcudUHjFbCERPbl8A5W3NDG026Xeb2dVi0J0t7Kqk1FQcxN4CKj/iolJTNDTOEx/mNGHx+XScOKvnGlNOhNuUo9ZitMj3IAHPK2+fdjnv3c97+2qMUMpacPNruJJXa9vIhVCFeN4yECthKAKEy3BO1hd9qFJtBSTqOkNMHsRamkl/PBA2YMQGrwvj1uk/eG4ItX8QGZBJjiHmNNrocMpeUrw1HWTOk9IrMldFxa7ShL5vqCpWjsVPLEtucgrV1ytIYIfhy/pDSh5yfn3Dc2uCBSjgTlQ7d6FO1v4jqWluVrq3Ikrsxk8FYZ5j3uBv9dD31qPS6trZKiwht5W24QD0KwHTcjdG0OYi+a3uVXZk+iMgvisjXRORzF/zfz4iIish78t8iIv9MRF4Wkd8VkQ9dfymrrbbaXbDrRAr/BvjnwC/3d4rINwN/Dfg/3d0/QNJ6+HbgLwL/Mv++hpVuQu/SuvShP3aHSTIIGjx+zvLdCsZ0V4Qi2GFMnQ/QEIhS+veO4BNUGcCJ1GqwMaYCcdIxS8piCJo8sw+g0VXw0enpizw6S6HaZjD4benTu4oZEDPlq3Vhc9Y2b2GkMvsKthXd5rleZQ4OHNOTRwyn6Uqt41ALpSLSZvNlqpHCqEqol8MsJtNPh9YN1VopMx3kmeGg6jmYDHm2Y0qZwvhGhTxPW0vMoi9ODKF0DzrIs6NECuTPTNvV0WhLpdR2kOcGZIpqagcCEnNSPwdRhzSdrdoWqQvRkQAbqa/tQ6hApiC+noM++DYJ6n0SiikRprPnJOWgQJ7TueGcY/u0TGLGtyUpl46hVRm7ELoOmTx46iXl/Fwhz0ZDY44i4LLWxbi5WOX8Insmgdls/xT4+8B/6u77QeCXs9zTJ0XkORF5SVVfuXIldfA/Ur7xxth2siee4rKmBf+Cqm+VXJVaS48ZpJNuK7agztzAkMPicTzAGlfG4bEqmAKSUdMVrHucZEPNifg0wJNnDE5P38PrX3kZgKMRpvo8W9MHY3OLqr71Br5KzM7pdogRk183zDNzQU1uEg/fpuT4sXUsEKktMaA6BdGO01Bkr6/acn/ToRvtHrpRbeJbLOjGw+wUdoNhU9CNjwyhCD8ZqR1FkAZkMooNDmMKv0Ks4bfrWaajoerNL+YgYLCNdEWZ6IlZqh9RU3kW0FiBTKWeUvkVQkM3bsXXUzHEwFBmFTxXqkcBxEiHbtQFkKlXj9p3ChehGwenTBndaHP6sFCPKtwKc0DzwJNuzzBHh3mfOiBTri1cx55VIeojwJdV9TN7kOA/A/xJ93cRmL3SKSxb//XrUr/gC8KRvVahRq1FpBhN7c06O9QrmHMO5zJhhtjKl+iCxYQlmctVxCxCN9VXwhZJX4qT0xcrBwIokpGOMRpMgSVbl+i5M0GpaiNrNUaqbF3UWDELYX5ancLR6QkPXntccQPoUny3Et9aV6fsvPcVNRhFSBwMJQ9fYhbKxKkdO3Tj0EGed1u832KOUhuWcUTGXLtwAzGjG4O0ukUq+i7RjcupydxecwGbuQ/F28SvkF6toRujAqYWN40aopSr9hKzUAhdQ/QNaRljei/ZKUzeczymL5IYaqvUh1BbsiBvqR7VCF0DoatD9E5hM26QOuA34TIJx0XqUQAb4gJKrbHxKwzOVnSjmcYluvEw1xRokOc6OXkNe9tOQUSOgI8D33fRf19w34UlDhH5MeDHAL7ppZfe7jJWW221d8meJVL4NuD9QIkS/izwKRH5bp5VYPbPf1Cr7+g5AqCCX/Z1IZfKSYZxSAMhBweHjRbcShLqACwGk6/MtstnQcHsxSldKHeRiZiWPiAgWnkDxIycnr4IwPTmq9ghh4izqYIdYlPoHKtmZmvJGWNrJTz6yFCUffxDQq4pGKNYInMedpF7p+BKakDNP5xzTPnKMs8zmxw1xYTkqpV5a5pgrUFLAIPZOCjoxnGDlLRk2iHeM2csf0I3pihmGAZ2PgOZxogrtHmhoRuTyMweE9MC3ZjrANJqOoljoQcymRr5WDWVNyFxMBS1p8bd6LW1fYPG8+pRFGCVVJJ7RZvepR3eUj2qtArnGAjl+Ey1E7TbzbUDAeAvUI9Ke9HO7RBCx24VF6PUzg3n1KMgoRttRTc26veDdzNSUNXPAu8tf4vIF4HvUtXXROQTwE+IyK+RCoxvXquesHj92Fp1fYJ9SfqQ6n+C5DDbDoe1aJhos7vBqa6YeV2Zuotl26AERQaDSAenloGT7BRee/NrHOfv9O5MFk4hoY5LaN2KW8b0zqhrwzI0Es5px8GBZcr8/ofPz8SY4cgdOs46VzhS8N5z2HKMvP4O7tntUp067NCNOgxIxiyoOVtMTQ7DKWFMkzfjOLJ9XCDPfg/dmPfSpLC+pBbGCKFQkMWA2vPcjSphD7OgjV8hWnyGkPfU70RqcZl4Pi1cqEeVgqwbmLNStsbG3TgYl9SjuhZvKUh7natTmLwnkNGNqvWipJL4IwrXgVygHgXnIc8u78UuYxaaetTBOfUogLjd4vLgmtjQ0I3m+l/167QkfxX4X8B3iMj/FZEffYuH/2fgC8DLwC8Af+/aK1lttdXuhD2rwGz//+/rbivw48+ykBbNt35jfy1DGrVXupya+njVhjGPMaJlIIYmkJof3K+7/t4fm75OFFFblVhE5kohhrT04csaOS2qTLRIIRUaDWLKWG5bp6J1iCcGrfMNRsYaKczTloPNKW/mSOFonhuFF23wyVpXr84hxoqmE+PSXmofBZX93AcyZcq1ccCWopsxC3q2g8PnmTK6cTMMaFF4ojU59jkWVGLHWt0AZyF6XM/y7Esu081BiCb1KCn8CqZGl2k4quyFrQswYuseGZOAY716VEEejlk9qnwWhbV7HDdJParwEhxR0wdE6tXVkEJ9gGAdriI1E7rRLXQml+pR7bNo6cMmK5Rp0KweldcpLest6lHAEt043Kuo2ZJGXMfuDKKx5fEG8jQd4jBSJLzaQE2Moc8m0hDL3JxCvZ+WK+vi37efPpy3Ln3oyFxUHIeHCT8wbI7xeVDFjIa4LSdoQjeaTAWvwTQ2a7o+vaQvMySYrJ/Thz1PW+6fvMCrr6cWIdOuVsVVm1MTMY3+a2qYBeuGhG7sUrCau0OFPHur2E3GHwxLdKOZp0olr0iTMBvfrB2f4CdCN+hU0Y0iRCPY2KYmi2xeCI3l2VpXIc8ClT8h/W7qUUZcpx7V8AwiWnEK1lq8FoFaOaceNWWui/udelSkcT6AsFSPCvVbaZ2paElrTK2PBOuIOtXH+HlG9XL1qPT56aLWUIhwDPEt1KOW6MaKWThZKlNf19aBqNVWW21hdyRS6AZnYuu5J3Rihy68gOugjFQXRJz3MyEDaZxpQKaGeEg9c+1C57dTeCzHbEhLg8EQ6zy+qZiF09MXOXstwTbGAcKuHMMhtnUZErqxYOSbQlZifSup1FCjZ51mYpg4yAIy09PHuJOMbhxc7b8jgss9/yBzjRSGim5s3Y9yTNXGoIxRJHdPZDOgGbPAcJA7EBkDETp04/CwzUHMhnCQw+8L0I2uE71p6Ma4RDfW+QzTFXMjV6lHpfsby7N1lrmgGwe5VD1KozLmc24roZ6LPgZkoR7laxRlrCXMfRRS0I0jSpaOd47d2SXqUTF0ojFLdGMdmrLuAvWoFLlNuyW6saQPg4aKmo18A6YPxVRDDZm0A4WIsVzE19iel0PeeSZu8vO7iuuysnB5KLXP2XBVe5KCtayhbY9ufJE3X/0SAIejZS45rDjEuY5q4GJ0ozFSwSdRI0YKkGlm3u04yOpF05PHDLWmcoDatk+2m8Cr6MaeVyAtaPF++ppORTeOA7GiGzfomcWUbojfcZCdwjRKTR+ePDWEw7aX7RC5q3SpetR57sYQl0Cmq9Sj6vssbxVbAWdopOdXWKpHzYxjbiNKE+sNITDagXCBelSqLRRuB9MmJtWk9i+A0cvVo8LSKfTnXA953leP2hymNnxRjwIyujGvcbdFbEY3NmjplbamD6utttrC7kyk0HXm0VKQSlM7QPLAvosUerAHdJGCn84VG8vr1vs6mraSPlwdEVy27jQHIaUgugd5ngp82TWQlEYDklIIADGhYRa6tRnTaOJSWFnmILapA3GcrhSPHjzmJIevoe+kdMNdYm3tsQfvMaPtRtPlXDoGqeCYo/I0B5HTAjMkQlcpXA9+x9FhgjzH0WHGEv4PxFAIXfUc5LlKysV9SbkMyukJXUN3/cpj1A12sZSUa8NRoUYHGqUJ5mhY8CssJeU8h+SCqtktIM8Hl0jKmSopl95doWnTEAiFvZlwpaQcXA55Hol7knKhRiRFUg6WkGfdnmEzg3iRlbuO3Rmn0IKWpqCcgEwF4HLd9GFq4SedU2hzMhg5n0Jc5hSukz4YsV3rVNEyV+EOOTlJepG7Jw+wY2k1pjkIqUCW0NpjGppTsKZDNypjRjfO8yP8tOXec/moYSJk0Q/xJ2gRIDHUFMFZVwE68zwzDGMHDGstvcRVUSreSujmIDSnBYwHCd2YnUJqT+bweTglDq8BCd04Z43IMDRm5RhZqkedm4Mon1/EdhOTDd0YawcCIGZ+BUj07yVUFhOR2M1B2DIHkeYWgr9IPWrmlORsTYdujMQr1aMga5vm9zJ3TkF1vkA9qjyn1Rr69GFfQepy9SjHHM6jG+P2KTZ/l2Zd04fVVlvtGe3ORAqLq0DxajE2ElOxNUQzXS+4jsF23rViFsZNEwO5BMh03e7DZZDndGypI7rnIc8pUnjw+OscDrlCvk1AJqmj1LvKdaD4ujbpIM+RXnXaobPH56nJg41jl4FMm+deQLPStXZ8ENY55ind9t6z0UioYjK9auYlkGeb+BWACnleSsrlKvt4ShiTlP04juyeljkI6hxEiGHBr3CRpBwUabUOyNSJ2VwmKeeCYacF29JBngOt6BrPR377knKQiFsLfFpVr5aUA5jmOqI/hYDPqYiiWCsVTBRiG50uknLlc6p0cHtdiHOScjllGDZjkpQDmEOFPOvZWdUrLbJy1zG5SATlpk1EXgWeAK/d9lo6ew/req6yu7amdT1vbd+qqi9e9aA74RQAROS3VPW7bnsdxdb1XG13bU3ret4ZW2sKq6222sJWp7Daaqst7C45hX912wvYs3U9V9tdW9O6nnfA7kxNYbXVVrsbdpcihdVWW+0O2K07BRH5fhH5gywg87FbWsM3i8h/F5HPi8jvichP5vt/VkS+LCKfzj8fvsE1fVFEPpuP+1v5vhdE5L+KyB/l38/f0Fq+o9uDT4vIQxH5qZveH7lAmOiyPZFk76ow0SXr+Uci8vv5mL8uIs/l+98nImfdXv38O72ed8x67P9N/5CgSX8MfAAYgc8AH7yFdbwEfCjfPgH+EPgg8LPAz9zS3nwReM/eff8Q+Fi+/THg527pM/sK8K03vT/A9wIfAj531Z4AHwb+C2l45nuA37yh9Xwf4PLtn+vW877+cXf557Yjhe8GXlbVL6jqBPwaSVDmRk1VX1HVT+Xbj4DPk/Qq7pr9IPBL+fYvAX/zFtbwV4E/VtUv3fSBVfV/Al/fu/uyPanCRKr6SeA5EXlHtQQuWo+q/oY2+OAnSYzm31B2207hMvGYWzMReR/wncBv5rt+IoeCv3hT4Xo2BX5DRH5bkkYGwJ/WzI6df7/30me/e/ZDwK92f9/W/hS7bE/uwrn1d0nRSrH3i8jviMj/EJG/csNrubbdtlO4aODg1tohInIP+A/AT6nqQ5IW5rcBf4GkcvWPb3A5f0lVP0TS5/xxEfneGzz2hSYiI/AR4N/nu25zf66yWz23ROTjgAd+Jd/1CvAtqvqdwE8D/05ETm9qPW/HbtspXFs85t02ERlIDuFXVPU/AqjqV1U1aJol/gVSunMjpqr/L//+GvDr+dhfLSFw/v21m1pPth8APqWqX81ru7X96eyyPbm1c0tEPgr8deCHNRcUVHWnqq/n279NqqX9uZtYz9u123YK/xv4dhF5f74K/RDwiZtehKQRuX8NfF5V/0l3f5+D/i3gc/vPfZfWcywiJ+U2qXj1OdLefDQ/7KMsxX1vwv4OXepwW/uzZ5ftySeAH8ldiO/hGYSJnsVE5PuBfwB8RFWfdve/KJKINkTkAyRl9i+82+t5JrvtSiepSvyHJM/58Vtaw18mhZa/C3w6/3wY+LfAZ/P9nwBeuqH1fIDUifkM8HtlX4A/Bfw34I/y7xducI+OgNeB+919N7o/JIf0CjCTIoEfvWxPSOnDv8jn1WdJKmY3sZ6XSbWMch79fH7s386f5WeATwF/4zbO9ev8rIjG1VZbbWG3nT6sttpqd8xWp7DaaqstbHUKq6222sJWp7DaaqstbHUKq6222sJWp7DaaqstbHUKq6222sJWp7Daaqst7P8DNaW0Pjv4nYoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 图像预处理模块\n",
    "from keras.preprocessing import image      \n",
    "\n",
    "# 选择一张图像进行增强\n",
    "fnames = [os.path.join(train_cats_dir, fname) for fname in os.listdir(train_cats_dir)]\n",
    "img_path = fnames[3]\n",
    "img = image.load_img(img_path, target_size=(150, 150))\n",
    "\n",
    "# 形状变为 (1, 150, 150, 3) 的 Numpy 数组\n",
    "x = image.img_to_array(img)\n",
    "x = x.reshape((1,) + x.shape)\n",
    "\n",
    "i = 0\n",
    "for batch in datagen.flow(x, batch_size=1):\n",
    "    plt.figure(i)\n",
    "    implot = plt.imshow(image.array_to_img(batch[0]))\n",
    "    i += 1\n",
    "    if i % 4 == 0:\n",
    "        break\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 添加 Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_5 (Conv2D)            (None, 148, 148, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 74, 74, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 72, 72, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 36, 36, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 34, 34, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 17, 17, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 15, 15, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 512)               3211776   \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 3,453,121\n",
      "Trainable params: 3,453,121\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dropout(0.5))\n",
    "model.add(layers.Dense(512, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(lr=1e-4),\n",
    "              metrics=['acc'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练与验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# 训练数据使用增强\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255, \n",
    "    rotation_range=40, \n",
    "    width_shift_range=0.2, \n",
    "    height_shift_range=0.2, \n",
    "    shear_range=0.2, \n",
    "    zoom_range=0.2, \n",
    "    horizontal_flip=True)\n",
    "\n",
    "# 验证数据不能使用增强\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_dir, \n",
    "    target_size=(150, 150),\n",
    "    batch_size=32,\n",
    "    class_mode='binary')\n",
    "\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "    validation_dir, \n",
    "    target_size=(150, 150),\n",
    "    batch_size=32, \n",
    "    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "100/100 [==============================] - 243s 2s/step - loss: 0.6918 - acc: 0.5150 - val_loss: 0.6788 - val_acc: 0.5200\n",
      "Epoch 2/100\n",
      "100/100 [==============================] - 239s 2s/step - loss: 0.6742 - acc: 0.5688 - val_loss: 0.6710 - val_acc: 0.5760\n",
      "Epoch 3/100\n",
      "100/100 [==============================] - 229s 2s/step - loss: 0.6604 - acc: 0.5916 - val_loss: 0.6660 - val_acc: 0.5640\n",
      "Epoch 4/100\n",
      "100/100 [==============================] - 228s 2s/step - loss: 0.6473 - acc: 0.6106 - val_loss: 0.6244 - val_acc: 0.6520\n",
      "Epoch 5/100\n",
      "100/100 [==============================] - 235s 2s/step - loss: 0.6266 - acc: 0.6447 - val_loss: 0.6168 - val_acc: 0.6400\n",
      "Epoch 6/100\n",
      "100/100 [==============================] - 243s 2s/step - loss: 0.6217 - acc: 0.6491 - val_loss: 0.5945 - val_acc: 0.6710\n",
      "Epoch 7/100\n",
      "100/100 [==============================] - 241s 2s/step - loss: 0.6048 - acc: 0.6738 - val_loss: 0.5700 - val_acc: 0.7070\n",
      "Epoch 8/100\n",
      "100/100 [==============================] - 236s 2s/step - loss: 0.5950 - acc: 0.6812 - val_loss: 0.5931 - val_acc: 0.6710\n",
      "Epoch 9/100\n",
      "100/100 [==============================] - 237s 2s/step - loss: 0.5932 - acc: 0.6891 - val_loss: 0.5934 - val_acc: 0.6710\n",
      "Epoch 10/100\n",
      "100/100 [==============================] - 251s 3s/step - loss: 0.5832 - acc: 0.6916 - val_loss: 0.5947 - val_acc: 0.6690\n",
      "Epoch 11/100\n",
      "100/100 [==============================] - 258s 3s/step - loss: 0.5856 - acc: 0.6856 - val_loss: 0.5347 - val_acc: 0.7350\n",
      "Epoch 12/100\n",
      "100/100 [==============================] - 234s 2s/step - loss: 0.5652 - acc: 0.7100 - val_loss: 0.5601 - val_acc: 0.7110\n",
      "Epoch 13/100\n",
      "100/100 [==============================] - 243s 2s/step - loss: 0.5615 - acc: 0.7109 - val_loss: 0.5393 - val_acc: 0.7290\n",
      "Epoch 14/100\n",
      "100/100 [==============================] - 233s 2s/step - loss: 0.5487 - acc: 0.7234 - val_loss: 0.6797 - val_acc: 0.6100\n",
      "Epoch 15/100\n",
      "100/100 [==============================] - 242s 2s/step - loss: 0.5552 - acc: 0.7137 - val_loss: 0.5685 - val_acc: 0.6920\n",
      "Epoch 16/100\n",
      "100/100 [==============================] - 238s 2s/step - loss: 0.5571 - acc: 0.7094 - val_loss: 0.5148 - val_acc: 0.7540\n",
      "Epoch 17/100\n",
      "100/100 [==============================] - 232s 2s/step - loss: 0.5429 - acc: 0.7262 - val_loss: 0.5214 - val_acc: 0.7450\n",
      "Epoch 18/100\n",
      "100/100 [==============================] - 233s 2s/step - loss: 0.5349 - acc: 0.7381 - val_loss: 0.5480 - val_acc: 0.7160\n",
      "Epoch 19/100\n",
      "100/100 [==============================] - 232s 2s/step - loss: 0.5442 - acc: 0.7216 - val_loss: 0.5730 - val_acc: 0.6880\n",
      "Epoch 20/100\n",
      "100/100 [==============================] - 242s 2s/step - loss: 0.5416 - acc: 0.7197 - val_loss: 0.4946 - val_acc: 0.7610\n",
      "Epoch 21/100\n",
      "100/100 [==============================] - 263s 3s/step - loss: 0.5363 - acc: 0.7297 - val_loss: 0.4901 - val_acc: 0.7650\n",
      "Epoch 22/100\n",
      "100/100 [==============================] - 263s 3s/step - loss: 0.5269 - acc: 0.7300 - val_loss: 0.5971 - val_acc: 0.6770\n",
      "Epoch 23/100\n",
      "100/100 [==============================] - 252s 3s/step - loss: 0.5218 - acc: 0.7369 - val_loss: 0.4928 - val_acc: 0.7450\n",
      "Epoch 24/100\n",
      "100/100 [==============================] - 256s 3s/step - loss: 0.5198 - acc: 0.7369 - val_loss: 0.5447 - val_acc: 0.7190\n",
      "Epoch 25/100\n",
      "100/100 [==============================] - 267s 3s/step - loss: 0.5282 - acc: 0.7322 - val_loss: 0.5106 - val_acc: 0.7430\n",
      "Epoch 26/100\n",
      "100/100 [==============================] - 238s 2s/step - loss: 0.5112 - acc: 0.7531 - val_loss: 0.5074 - val_acc: 0.7530\n",
      "Epoch 27/100\n",
      "100/100 [==============================] - 234s 2s/step - loss: 0.5175 - acc: 0.7475 - val_loss: 0.5080 - val_acc: 0.7510\n",
      "Epoch 28/100\n",
      "100/100 [==============================] - 245s 2s/step - loss: 0.5152 - acc: 0.7438 - val_loss: 0.4775 - val_acc: 0.7600\n",
      "Epoch 29/100\n",
      "100/100 [==============================] - 247s 2s/step - loss: 0.5232 - acc: 0.7313 - val_loss: 0.4893 - val_acc: 0.7580\n",
      "Epoch 30/100\n",
      "100/100 [==============================] - 245s 2s/step - loss: 0.5087 - acc: 0.7506 - val_loss: 0.5426 - val_acc: 0.7100\n",
      "Epoch 31/100\n",
      "100/100 [==============================] - 244s 2s/step - loss: 0.5074 - acc: 0.7465 - val_loss: 0.4997 - val_acc: 0.7670\n",
      "Epoch 32/100\n",
      "100/100 [==============================] - 242s 2s/step - loss: 0.5082 - acc: 0.7547 - val_loss: 0.4991 - val_acc: 0.7630\n",
      "Epoch 33/100\n",
      "100/100 [==============================] - 230s 2s/step - loss: 0.4955 - acc: 0.7709 - val_loss: 0.4646 - val_acc: 0.7740\n",
      "Epoch 34/100\n",
      " 61/100 [=================>............] - ETA: 1:28 - loss: 0.5044 - acc: 0.7474"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-731a6d94de9d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     validation_steps=50)\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\python36\\lib\\site-packages\\keras\\legacy\\interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name +\n\u001b[0;32m     90\u001b[0m                               '` call to the Keras 2 API: ' + signature, stacklevel=2)\n\u001b[1;32m---> 91\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     93\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\python36\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[1;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[0;32m   1413\u001b[0m             \u001b[0muse_multiprocessing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1414\u001b[0m             \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1415\u001b[1;33m             initial_epoch=initial_epoch)\n\u001b[0m\u001b[0;32m   1416\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1417\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\python36\\lib\\site-packages\\keras\\engine\\training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[1;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[0;32m    211\u001b[0m                 outs = model.train_on_batch(x, y,\n\u001b[0;32m    212\u001b[0m                                             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 213\u001b[1;33m                                             class_weight=class_weight)\n\u001b[0m\u001b[0;32m    214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\python36\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[1;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[0;32m   1213\u001b[0m             \u001b[0mins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1214\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1215\u001b[1;33m         \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1216\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0munpack_singleton\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1217\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   2664\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2665\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2666\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2667\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2668\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   2634\u001b[0m                                 \u001b[0msymbol_vals\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2635\u001b[0m                                 session)\n\u001b[1;32m-> 2636\u001b[1;33m         \u001b[0mfetched\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2637\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2638\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\python36\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1397\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[0;32m   1398\u001b[0m               \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1399\u001b[1;33m               run_metadata_ptr)\n\u001b[0m\u001b[0;32m   1400\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1401\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "    train_generator, \n",
    "    steps_per_epoch=100,\n",
    "    epochs=100, \n",
    "    validation_data=validation_generator, \n",
    "    validation_steps=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('cats_and_dogs_small_2.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(1, len(acc) + 1)\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
